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Sustainable agriculture is becoming increasingly important in the face of growing environmental challenges. One key aspect of sustainable agriculture is managing soil resources effectively. In this context, digital soil mapping (DSM) has emerged as a powerful tool to understand soil variability better and inform land management decisions. This paper proposes a comprehensive data warehouse for DSM that supports climate change analysis. Our architecture integrates frequent itemset mining (FMI) and association rules mining (ARM) to extract insights from large-scale soil data. We review related studies in soil data warehousing and ARM, identify gaps, and propose a data warehouse architecture leveraging the galaxy multidimensional model for DSM based on the SCORPAN model, which incorporates all relevant soil forming factors. We employ and compare A-priori, FP-growth, and ECLAT algorithms to efficiently mine frequent itemsets and generate association rules. Our intensive experiments evaluation demonstrates that FP-growth outperforms the other algorithms in accuracy, scalability, and speed and requires less memory. Additionally, we utilized correlation metrics for ARM, such as lift, cosine, kulc, and Imbalance ratio, to obtain the most significant and relevant association rules. These rules provide valuable insights into the complex relationships between soil properties and environmental factors, which can inform land management decisions and improve sustainable agriculture practices. This work contributes to the growing body of research on DSM and data-driven approaches to sustainable agriculture.

Predicting transportation mode choice is a classic challenge of travel behavior research. Over the years, different theoretical concepts and modeling approaches have been applied. This paper elaborates the application of class association rules (CARs) and examines their predictive performance using data extracted from the 2015 National Dutch Travel Survey. To solve the problem how to activate rules that have high confidence but low support, the information gain (IG) concept is introduced in the model building process. The modeling process in this study first involves extracting frequent items from the data using the FP-Growth algorithm and deriving CARs from these frequent items. Next, the IG statistic is used to construct a novel model (named CARIG), which consists of a set of decision rules that formally represent behavioral scripts, for predicting individuals’ transportation mode choice. The performance of CARIG is compared with the performance of conventional class-based association rules (CBA), decision trees (DT), a convolutional neural network (CNN) and a logistic regression (LR) model. In addition, a 10-fold cross validation test using a grid search parameter optimization method is conducted to validate the proposed approach. The results show that the proposed method is promising in predicting transportation mode choices observed in the national travel survey data.

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.

The organizations extremely are competitive surroundings, in which the administration of the relations with the clients acquires a signicant, especially in the related thing to the loyalty, the increase of the sales and the increase of the yield. Consequently, the organizations are called to administer the relations with their clients from the strategic point of view, but also from the tactical and operative point of view. By such reason, the technology is one of those components that such management supports and that its effective implementation potentialises. The present article sets out the results of a degree project, that approached problematic concerning the management of relations with the clients in a restaurant of fast meals of Tuluá, based on the methodology the SCRUM and techniques of mining of data, the result was a computer science system for the support to the decision making in the context of the intelligence of businesses. The integration of the algorithms of near neighbors (KNN) and FP-GROWTH stands out, with the purpose of emitting recommendations in relation to the preferences of the consumers with base in the composition of products more consumed by them. In addition, once integrated the algorithms they tried on in four different scenes, which allowed to conclude that such integration does not have associate a high computer cost., As organizações são encontradas resumo-atualmente um ambiente superiormente competitivo, onde a administração das relações com os clientes percebe um valor significativo, especialmente que está relatório délisation, o aumento das vendas e o crescimento da rentabilidade. Por conseguinte, as organizações são chamadas de administrar as relações com os seus clientes do ponto de vista estratégico, mas também o ponto de vista tático e operacional. Por tal motivo, a tecnologia é um destes componentes que apoia tal gestão e que potencialize a sua aposta em obra efetiva. O presente artigo expõe os resultados de um projeto de grau [1], que abordou a problemática relativa à gestão de relações com os clientes num restaurante de restaurações rápidas de Tuluá, baseado a metodologia SCRUM e técnicas de indústria mineira de dados, o resultado foi um sistema informático para o apoio à tomada de decisão no contexto da inteligência de negócios. Sublinha-se a integração dos algoritmos de vizinhos próximos (KNN) e de FP-GROWTH, a em de emitir recomendações em relação às preferências dos consumidores com base na composição dos produtos mais consumidos por eles. Além disso, uma vez integrados os algoritmos foram provados em quatro cenas diferentes, que permitiu concluir que tal integração não associou um custo computacional que importa., Actualmente las organizaciones se encuentran un entorno sumamente competitivo, en el que la administración de las relaciones con los clientes cobra un valor significativo, especialmente en lo relacionado a la fidelización, el aumento de las ventas y el incremento de la rentabilidad. En consecuencia, las organizaciones están llamadas a administrar las relaciones con sus clientes desde el punto de vista estratégico, pero también desde el punto de vista táctico y operativo. Por tal motivo, la tecnología es uno de esos componentes que apoya tal gestión y que potencializa su efectiva 28implementación. El presente artículo expone los resultados de un proyecto de grado , que abordó la problemática concerniente a la gestión de relaciones con los clientes en un restaurante de comidas rápidas de Tuluá, basado en la metodología SCRUM y técnicas de minería de datos, el resultado fue un sistema informático para el apoyo a la toma de decisiones en el contexto de la inteligencia de negocios. Se destaca la integración de los algoritmos de vecinos cercanos (KNN) y FP-GROWTH, con el fin de emitir recomendaciones en relación con las preferencias de los consumidores con base en la composición de los productos más consumidos por ellos. Además, una vez integrados los algoritmos se probaron en cuatro escenarios diferentes, lo que permitió concluir que tal integración no tiene asociado un costo computacional elevado.

The organizations extremely are competitive surroundings, in which the administration of the relations with the clients acquires a signicant, especially in the related thing to the loyalty, the increase of the sales and the increase of the yield. Consequently, the organizations are called to administer the relations with their clients from the strategic point of view, but also from the tactical and operative point of view. By such reason, the technology is one of those components that such management supports and that its effective implementation potentialises. The present article sets out the results of a degree project, that approached problematic concerning the management of relations with the clients in a restaurant of fast meals of Tuluá, based on the methodology the SCRUM and techniques of mining of data, the result was a computer science system for the support to the decision making in the context of the intelligence of businesses. The integration of the algorithms of near neighbors (KNN) and FP-GROWTH stands out, with the purpose of emitting recommendations in relation to the preferences of the consumers with base in the composition of products more consumed by them. In addition, once integrated the algorithms they tried on in four different scenes, which allowed to conclude that such integration does not have associate a high computer cost., As organizações são encontradas resumo-atualmente um ambiente superiormente competitivo, onde a administração das relações com os clientes percebe um valor significativo, especialmente que está relatório délisation, o aumento das vendas e o crescimento da rentabilidade. Por conseguinte, as organizações são chamadas de administrar as relações com os seus clientes do ponto de vista estratégico, mas também o ponto de vista tático e operacional. Por tal motivo, a tecnologia é um destes componentes que apoia tal gestão e que potencialize a sua aposta em obra efetiva. O presente artigo expõe os resultados de um projeto de grau [1], que abordou a problemática relativa à gestão de relações com os clientes num restaurante de restaurações rápidas de Tuluá, baseado a metodologia SCRUM e técnicas de indústria mineira de dados, o resultado foi um sistema informático para o apoio à tomada de decisão no contexto da inteligência de negócios. Sublinha-se a integração dos algoritmos de vizinhos próximos (KNN) e de FP-GROWTH, a em de emitir recomendações em relação às preferências dos consumidores com base na composição dos produtos mais consumidos por eles. Além disso, uma vez integrados os algoritmos foram provados em quatro cenas diferentes, que permitiu concluir que tal integração não associou um custo computacional que importa., Actualmente las organizaciones se encuentran un entorno sumamente competitivo, en el que la administración de las relaciones con los clientes cobra un valor significativo, especialmente en lo relacionado a la fidelización, el aumento de las ventas y el incremento de la rentabilidad. En consecuencia, las organizaciones están llamadas a administrar las relaciones con sus clientes desde el punto de vista estratégico, pero también desde el punto de vista táctico y operativo. Por tal motivo, la tecnología es uno de esos componentes que apoya tal gestión y que potencializa su efectiva 28implementación. El presente artículo expone los resultados de un proyecto de grado , que abordó la problemática concerniente a la gestión de relaciones con los clientes en un restaurante de comidas rápidas de Tuluá, basado en la metodología SCRUM y técnicas de minería de datos, el resultado fue un sistema informático para el apoyo a la toma de decisiones en el contexto de la inteligencia de negocios. Se destaca la integración de los algoritmos de vecinos cercanos (KNN) y FP-GROWTH, con el fin de emitir recomendaciones en relación con las preferencias de los consumidores con base en la composición de los productos más consumidos por ellos. Además, una vez integrados los algoritmos se probaron en cuatro escenarios diferentes, lo que permitió concluir que tal integración no tiene asociado un costo computacional elevado.

The organizations extremely are competitive surroundings, in which the administration of the relations with the clients acquires a signicant, especially in the related thing to the loyalty, the increase of the sales and the increase of the yield. Consequently, the organizations are called to administer the relations with their clients from the strategic point of view, but also from the tactical and operative point of view. By such reason, the technology is one of those components that such management supports and that its effective implementation potentialises. The present article sets out the results of a degree project, that approached problematic concerning the management of relations with the clients in a restaurant of fast meals of Tuluá, based on the methodology the SCRUM and techniques of mining of data, the result was a computer science system for the support to the decision making in the context of the intelligence of businesses. The integration of the algorithms of near neighbors (KNN) and FP-GROWTH stands out, with the purpose of emitting recommendations in relation to the preferences of the consumers with base in the composition of products more consumed by them. In addition, once integrated the algorithms they tried on in four different scenes, which allowed to conclude that such integration does not have associate a high computer cost., As organizações são encontradas resumo-atualmente um ambiente superiormente competitivo, onde a administração das relações com os clientes percebe um valor significativo, especialmente que está relatório délisation, o aumento das vendas e o crescimento da rentabilidade. Por conseguinte, as organizações são chamadas de administrar as relações com os seus clientes do ponto de vista estratégico, mas também o ponto de vista tático e operacional. Por tal motivo, a tecnologia é um destes componentes que apoia tal gestão e que potencialize a sua aposta em obra efetiva. O presente artigo expõe os resultados de um projeto de grau [1], que abordou a problemática relativa à gestão de relações com os clientes num restaurante de restaurações rápidas de Tuluá, baseado a metodologia SCRUM e técnicas de indústria mineira de dados, o resultado foi um sistema informático para o apoio à tomada de decisão no contexto da inteligência de negócios. Sublinha-se a integração dos algoritmos de vizinhos próximos (KNN) e de FP-GROWTH, a em de emitir recomendações em relação às preferências dos consumidores com base na composição dos produtos mais consumidos por eles. Além disso, uma vez integrados os algoritmos foram provados em quatro cenas diferentes, que permitiu concluir que tal integração não associou um custo computacional que importa., Actualmente las organizaciones se encuentran un entorno sumamente competitivo, en el que la administración de las relaciones con los clientes cobra un valor significativo, especialmente en lo relacionado a la fidelización, el aumento de las ventas y el incremento de la rentabilidad. En consecuencia, las organizaciones están llamadas a administrar las relaciones con sus clientes desde el punto de vista estratégico, pero también desde el punto de vista táctico y operativo. Por tal motivo, la tecnología es uno de esos componentes que apoya tal gestión y que potencializa su efectiva 28implementación. El presente artículo expone los resultados de un proyecto de grado , que abordó la problemática concerniente a la gestión de relaciones con los clientes en un restaurante de comidas rápidas de Tuluá, basado en la metodología SCRUM y técnicas de minería de datos, el resultado fue un sistema informático para el apoyo a la toma de decisiones en el contexto de la inteligencia de negocios. Se destaca la integración de los algoritmos de vecinos cercanos (KNN) y FP-GROWTH, con el fin de emitir recomendaciones en relación con las preferencias de los consumidores con base en la composición de los productos más consumidos por ellos. Además, una vez integrados los algoritmos se probaron en cuatro escenarios diferentes, lo que permitió concluir que tal integración no tiene asociado un costo computacional elevado.

The organizations extremely are competitive surroundings, in which the administration of the relations with the clients acquires a signicant, especially in the related thing to the loyalty, the increase of the sales and the increase of the yield. Consequently, the organizations are called to administer the relations with their clients from the strategic point of view, but also from the tactical and operative point of view. By such reason, the technology is one of those components that such management supports and that its effective implementation potentialises. The present article sets out the results of a degree project, that approached problematic concerning the management of relations with the clients in a restaurant of fast meals of Tuluá, based on the methodology the SCRUM and techniques of mining of data, the result was a computer science system for the support to the decision making in the context of the intelligence of businesses. The integration of the algorithms of near neighbors (KNN) and FP-GROWTH stands out, with the purpose of emitting recommendations in relation to the preferences of the consumers with base in the composition of products more consumed by them. In addition, once integrated the algorithms they tried on in four different scenes, which allowed to conclude that such integration does not have associate a high computer cost., As organizações são encontradas resumo-atualmente um ambiente superiormente competitivo, onde a administração das relações com os clientes percebe um valor significativo, especialmente que está relatório délisation, o aumento das vendas e o crescimento da rentabilidade. Por conseguinte, as organizações são chamadas de administrar as relações com os seus clientes do ponto de vista estratégico, mas também o ponto de vista tático e operacional. Por tal motivo, a tecnologia é um destes componentes que apoia tal gestão e que potencialize a sua aposta em obra efetiva. O presente artigo expõe os resultados de um projeto de grau [1], que abordou a problemática relativa à gestão de relações com os clientes num restaurante de restaurações rápidas de Tuluá, baseado a metodologia SCRUM e técnicas de indústria mineira de dados, o resultado foi um sistema informático para o apoio à tomada de decisão no contexto da inteligência de negócios. Sublinha-se a integração dos algoritmos de vizinhos próximos (KNN) e de FP-GROWTH, a em de emitir recomendações em relação às preferências dos consumidores com base na composição dos produtos mais consumidos por eles. Além disso, uma vez integrados os algoritmos foram provados em quatro cenas diferentes, que permitiu concluir que tal integração não associou um custo computacional que importa., Actualmente las organizaciones se encuentran un entorno sumamente competitivo, en el que la administración de las relaciones con los clientes cobra un valor significativo, especialmente en lo relacionado a la fidelización, el aumento de las ventas y el incremento de la rentabilidad. En consecuencia, las organizaciones están llamadas a administrar las relaciones con sus clientes desde el punto de vista estratégico, pero también desde el punto de vista táctico y operativo. Por tal motivo, la tecnología es uno de esos componentes que apoya tal gestión y que potencializa su efectiva 28implementación. El presente artículo expone los resultados de un proyecto de grado , que abordó la problemática concerniente a la gestión de relaciones con los clientes en un restaurante de comidas rápidas de Tuluá, basado en la metodología SCRUM y técnicas de minería de datos, el resultado fue un sistema informático para el apoyo a la toma de decisiones en el contexto de la inteligencia de negocios. Se destaca la integración de los algoritmos de vecinos cercanos (KNN) y FP-GROWTH, con el fin de emitir recomendaciones en relación con las preferencias de los consumidores con base en la composición de los productos más consumidos por ellos. Además, una vez integrados los algoritmos se probaron en cuatro escenarios diferentes, lo que permitió concluir que tal integración no tiene asociado un costo computacional elevado.

Teknolojinin gelişmesiyle verilerin miktarında ve çeşitliliğinde bir artış gözlemlenmiştir. Bu artış, veri işleme yöntemlerinde yeni ihtiyaçları gündeme getirmiştir. Veri madenciliği bu ihtiyaçlara cevap verebilmek amacıyla geliştirilen büyük veri ambarlarından yararlı bilgi elde etme sürecidir. Veri madenciliği kapsamında, çoklukla gereksinim duyulan ve araştırılan konulardan biri sık öge küme madenciliğidir. Sık öge küme madenciliği için farklı algoritmalar üretilmekte ve incelenmektedir. Ancak, fiziksel kapasitelerin sınırlı olmasından dolayı seri algoritmalar artan veri miktarını karşılamaya yetmemektedir. Bu durum paralel hesaplamayı gündeme getirir. Paralel algoritmalarla, zamandan ve kaynaktan (depolama, hafıza gibi) tasarruf edebilmek amaçlanır. FP-Growth, sık öge kümelerini bulmak için geliştirilen bir veri madenciliği algoritmasıdır. Literatürde FP-Growth, işlenecek veritabanını sadece iki kez tarama ve sık ögeleri bulurken kullandığı sık örüntü ağacı özelliği ile bilinir. Kullandığı bu ağaç yapısı ile daha verimli bir algoritma sunar. Bu çalışma içerisinde FP-Growth algoritmasına, paralel programlar yazmak için geliştirilen bir kütüphane olan MPI ile kodlanan üç farklı paralel yaklaşım önerilmektedir. Bu yaklaşımlarda temel alınan şey, paralel uygulama sırasında düğümlerin tümünde veritabanının mevcut olup olmadığıdır. Ek olarak, paralellik için görev dağılımı esas alındığından görev dağılımının statik ya da dinamik olması da diğer bir parametredir. Bu yaklaşımların iki farklı öbek bilgisayar üzerinde performans analizi gerçekleştirilmiştir. Çıkan sonuçlara göre, veritabanının mevcut olduğu iki yaklaşımın ağaç gönderimi esasına dayanan diğer yaklaşıma göre daha iyi sonuç verdiği gözlemlenmektedir. Her ikisi için de, paralel algoritma iki işlemciyle çalıştığı durumda bile çalışma zamanını dörtte bir oranında azaltmaktadır. Buna ek olarak, daha iyi sonuç veren iki algoritma içerisinde görev dağılımının dinamik olarak gerçekleştiği durum statik görev dağılım, With the development of technology, there is an increase in size and complexity of data. The increase bring about new needs in the process of data processing. Data mining, aimed to satisfy these needs, is the process of acquiring useful information from large data warehouses. In data mining, the purpose is usually to find the frequent items which is called frequent itemset mining (FIM). Different algorithms are developed and studied for this purpose. Because of the limited physical capacity, serial algorithms aren’t sufficient when solving large data problems. So, paralel computing becomes important. With parallel algorithms, it is aimed to save on time and sources (like storage and memory). FP-Growth is a data mining algorithm that is developed to find frequent itemsets. FP-Growth is known with performing only two scans while reading database and using frequent pattern tree to find frequent items. Because of this data structure, it becomes more efficient than other frequent itemset mining algorithms. In the study, there are three different parallel approaches implemented in MPI, a library that is used to write parallel programs. The first basis thing for the approaches is whether there is a database on all of the nodes or not. The second thing is how task parallelism is done. For task, there are static and dynamical approaches. These are tested and analysed in two different clusters. According to the results, two of them are better than the other one which is sent database (tree) to the other processors. For both of them, the running time of the parallel algorithms is decreased by one forth by using two nodes. In addition to that, dynamical task parallelism is better than the static one in better algorithms.

Teknolojinin gelişmesiyle verilerin miktarında ve çeşitliliğinde bir artış gözlemlenmiştir. Bu artış, veri işleme yöntemlerinde yeni ihtiyaçları gündeme getirmiştir. Veri madenciliği bu ihtiyaçlara cevap verebilmek amacıyla geliştirilen büyük veri ambarlarından yararlı bilgi elde etme sürecidir. Veri madenciliği kapsamında, çoklukla gereksinim duyulan ve araştırılan konulardan biri sık öge küme madenciliğidir. Sık öge küme madenciliği için farklı algoritmalar üretilmekte ve incelenmektedir. Ancak, fiziksel kapasitelerin sınırlı olmasından dolayı seri algoritmalar artan veri miktarını karşılamaya yetmemektedir. Bu durum paralel hesaplamayı gündeme getirir. Paralel algoritmalarla, zamandan ve kaynaktan (depolama, hafıza gibi) tasarruf edebilmek amaçlanır. FP-Growth, sık öge kümelerini bulmak için geliştirilen bir veri madenciliği algoritmasıdır. Literatürde FP-Growth, işlenecek veritabanını sadece iki kez tarama ve sık ögeleri bulurken kullandığı sık örüntü ağacı özelliği ile bilinir. Kullandığı bu ağaç yapısı ile daha verimli bir algoritma sunar. Bu çalışma içerisinde FP-Growth algoritmasına, paralel programlar yazmak için geliştirilen bir kütüphane olan MPI ile kodlanan üç farklı paralel yaklaşım önerilmektedir. Bu yaklaşımlarda temel alınan şey, paralel uygulama sırasında düğümlerin tümünde veritabanının mevcut olup olmadığıdır. Ek olarak, paralellik için görev dağılımı esas alındığından görev dağılımının statik ya da dinamik olması da diğer bir parametredir. Bu yaklaşımların iki farklı öbek bilgisayar üzerinde performans analizi gerçekleştirilmiştir. Çıkan sonuçlara göre, veritabanının mevcut olduğu iki yaklaşımın ağaç gönderimi esasına dayanan diğer yaklaşıma göre daha iyi sonuç verdiği gözlemlenmektedir. Her ikisi için de, paralel algoritma iki işlemciyle çalıştığı durumda bile çalışma zamanını dörtte bir oranında azaltmaktadır. Buna ek olarak, daha iyi sonuç veren iki algoritma içerisinde görev dağılımının dinamik olarak gerçekleştiği durum statik görev dağılım, With the development of technology, there is an increase in size and complexity of data. The increase bring about new needs in the process of data processing. Data mining, aimed to satisfy these needs, is the process of acquiring useful information from large data warehouses. In data mining, the purpose is usually to find the frequent items which is called frequent itemset mining (FIM). Different algorithms are developed and studied for this purpose. Because of the limited physical capacity, serial algorithms aren’t sufficient when solving large data problems. So, paralel computing becomes important. With parallel algorithms, it is aimed to save on time and sources (like storage and memory). FP-Growth is a data mining algorithm that is developed to find frequent itemsets. FP-Growth is known with performing only two scans while reading database and using frequent pattern tree to find frequent items. Because of this data structure, it becomes more efficient than other frequent itemset mining algorithms. In the study, there are three different parallel approaches implemented in MPI, a library that is used to write parallel programs. The first basis thing for the approaches is whether there is a database on all of the nodes or not. The second thing is how task parallelism is done. For task, there are static and dynamical approaches. These are tested and analysed in two different clusters. According to the results, two of them are better than the other one which is sent database (tree) to the other processors. For both of them, the running time of the parallel algorithms is decreased by one forth by using two nodes. In addition to that, dynamical task parallelism is better than the static one in better algorithms.

La detección de alteraciones de la salud de los animales mediante pruebas de laboratorio es un proceso complejo que implica relacionar diversas variables y factores clínicos de los individuos; por ello, en esta investigación se evaluaron técnicas de inteligencia computacional que contribuyeron a la identificación de patrones de comportamiento de las alteraciones detectadas en las pruebas de hemograma aplicadas en bovinos. Aunque diversos algoritmos de inteligencia computacional son utilizados en la solución de problemas médicos, no se encontró registro de investigaciones en procesos médicos veterinarios. Una vez hecha una minuciosa caracterización de las variables y la evaluación de las técnicas de inteligencia computacional, se determinó que el algoritmo que mejor se ajusta al propósito de análisis de datos planteado es FP-Growth.

La detección de alteraciones de la salud de los animales mediante pruebas de laboratorio es un proceso complejo que implica relacionar diversas variables y factores clínicos de los individuos; por ello, en esta investigación se evaluaron técnicas de inteligencia computacional que contribuyeron a la identificación de patrones de comportamiento de las alteraciones detectadas en las pruebas de hemograma aplicadas en bovinos. Aunque diversos algoritmos de inteligencia computacional son utilizados en la solución de problemas médicos, no se encontró registro de investigaciones en procesos médicos veterinarios. Una vez hecha una minuciosa caracterización de las variables y la evaluación de las técnicas de inteligencia computacional, se determinó que el algoritmo que mejor se ajusta al propósito de análisis de datos planteado es FP-Growth.

Táto bakalárska práca rieši problematiku získavania znalostí z webových logov. Zdroj dát v podobe webových prístupových logov umožnuje po vhodnom predspracovaní použitie mnohých techník, ktoré sú určené pre získavanie znalostí. Aplikáciou týchto techník na predspracované dáta je možné klasifikovať užívateľské správanie do skupín, vyhľadať zaujímavé asociácie v správaní užívateľov, či nájsť v bežnom užívateľskom správaní predom neznáme sekvencie., This bachelor thesis deals with the problem of knowledge discovery from web logs. The data source in the form of web access logs allows, after appropriate preprocessing, the use of a number of techniques that are designed to deal with knowledge discovery. By applying these techniques to preprocessed data, it is possible to classify user behavior into groups, to discover interesting associations in user behavior, or to discover previously unknown sequences in common user behavior.

Cílem této bakalářské práce je návrh a implementace aplikace umožňující porovnávat výkonnost a časovou náročnost zvolených algoritmů pro dolování frekventovaných množin a asociačních pravidel. Pro demonstraci byly vybrány dolovací algoritmy Apriori, AprioriTIDList, AprioriItemSet a metoda s využitím FP-stromu. Testování probíhalo nad různými objemy dat a s různými hodnotami minimální podpory a spolehlivosti. Aplikace je implementována v objektově orientovaném jazyce C# a jako zdroj dat slouží relační databáze na MS SQL Server 2008., The main goal of this bachelor's thesis is design and implementation of the application that provides a comparison of the performance and time consumption of given algorithms for mining of the frequent itemsets and the association rules. For demonstration, the mining algorithms Apriori, AprioriTIDList, AprioriItemSet and the method using FP-tree were chosen. The tests were executed over various amounts of data and with different minimum support and confidence values as well. The application was implemented in the object oriented language C# and the relational database provided by MS SQL Server 2008 is used as the data source.

Táto bakalárska práca rieši problematiku získavania znalostí z webových logov. Zdroj dát v podobe webových prístupových logov umožnuje po vhodnom predspracovaní použitie mnohých techník, ktoré sú určené pre získavanie znalostí. Aplikáciou týchto techník na predspracované dáta je možné klasifikovať užívateľské správanie do skupín, vyhľadať zaujímavé asociácie v správaní užívateľov, či nájsť v bežnom užívateľskom správaní predom neznáme sekvencie., This bachelor thesis deals with the problem of knowledge discovery from web logs. The data source in the form of web access logs allows, after appropriate preprocessing, the use of a number of techniques that are designed to deal with knowledge discovery. By applying these techniques to preprocessed data, it is possible to classify user behavior into groups, to discover interesting associations in user behavior, or to discover previously unknown sequences in common user behavior.

Dolování frekventovaných vzorů z databází je již dobře prozkoumanou oblastí. Jak se však ukázalo, tyto algoritmy nejsou příliš vhodné pro zpracování proudu dat. Při dolování frekventovaných množin v proudu dat se musí udržovat kromě jednotlivých množin i jejich historie - a to nejen těch frekventovaných, ale i potenciálně frekventovaných, neboť nefrekventovaná množina se může stát časem frekventovanou. To zvyšuje nároky jak na paměť, tak na výpočetní výkon. Tato práce popisuje dva algoritmy: Lossy Counting a FP-stream. Součástí je také efektivní implementace těchto algoritmů v jazyce C# a jejich porovnání na základě měření., Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunately, these algorithms are not suitable for data stream processing. In frequent-pattern mining from data streams, it is important to manage sets of items and also their history. There are several reasons for this; it is not just the history of frequent items, but also the history of potentially frequent sets that can become frequent later. This requires more memory and computational power. This thesis describes two algorithms: Lossy Counting and FP-stream. An effective implementation of these algorithms in C# is an integral part of this thesis. In addition, the two algorithms have been compared.

Cílem této bakalářské práce je návrh a implementace aplikace umožňující porovnávat výkonnost a časovou náročnost zvolených algoritmů pro dolování frekventovaných množin a asociačních pravidel. Pro demonstraci byly vybrány dolovací algoritmy Apriori, AprioriTIDList, AprioriItemSet a metoda s využitím FP-stromu. Testování probíhalo nad různými objemy dat a s různými hodnotami minimální podpory a spolehlivosti. Aplikace je implementována v objektově orientovaném jazyce C# a jako zdroj dat slouží relační databáze na MS SQL Server 2008., The main goal of this bachelor's thesis is design and implementation of the application that provides a comparison of the performance and time consumption of given algorithms for mining of the frequent itemsets and the association rules. For demonstration, the mining algorithms Apriori, AprioriTIDList, AprioriItemSet and the method using FP-tree were chosen. The tests were executed over various amounts of data and with different minimum support and confidence values as well. The application was implemented in the object oriented language C# and the relational database provided by MS SQL Server 2008 is used as the data source.

Dolování frekventovaných vzorů z databází je již dobře prozkoumanou oblastí. Jak se však ukázalo, tyto algoritmy nejsou příliš vhodné pro zpracování proudu dat. Při dolování frekventovaných množin v proudu dat se musí udržovat kromě jednotlivých množin i jejich historie - a to nejen těch frekventovaných, ale i potenciálně frekventovaných, neboť nefrekventovaná množina se může stát časem frekventovanou. To zvyšuje nároky jak na paměť, tak na výpočetní výkon. Tato práce popisuje dva algoritmy: Lossy Counting a FP-stream. Součástí je také efektivní implementace těchto algoritmů v jazyce C# a jejich porovnání na základě měření., Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunately, these algorithms are not suitable for data stream processing. In frequent-pattern mining from data streams, it is important to manage sets of items and also their history. There are several reasons for this; it is not just the history of frequent items, but also the history of potentially frequent sets that can become frequent later. This requires more memory and computational power. This thesis describes two algorithms: Lossy Counting and FP-stream. An effective implementation of these algorithms in C# is an integral part of this thesis. In addition, the two algorithms have been compared.

Cílem této bakalářské práce je návrh a implementace aplikace umožňující porovnávat výkonnost a časovou náročnost zvolených algoritmů pro dolování frekventovaných množin a asociačních pravidel. Pro demonstraci byly vybrány dolovací algoritmy Apriori, AprioriTIDList, AprioriItemSet a metoda s využitím FP-stromu. Testování probíhalo nad různými objemy dat a s různými hodnotami minimální podpory a spolehlivosti. Aplikace je implementována v objektově orientovaném jazyce C# a jako zdroj dat slouží relační databáze na MS SQL Server 2008., The main goal of this bachelor's thesis is design and implementation of the application that provides a comparison of the performance and time consumption of given algorithms for mining of the frequent itemsets and the association rules. For demonstration, the mining algorithms Apriori, AprioriTIDList, AprioriItemSet and the method using FP-tree were chosen. The tests were executed over various amounts of data and with different minimum support and confidence values as well. The application was implemented in the object oriented language C# and the relational database provided by MS SQL Server 2008 is used as the data source.

Táto bakalárska práca rieši problematiku získavania znalostí z webových logov. Zdroj dát v podobe webových prístupových logov umožnuje po vhodnom predspracovaní použitie mnohých techník, ktoré sú určené pre získavanie znalostí. Aplikáciou týchto techník na predspracované dáta je možné klasifikovať užívateľské správanie do skupín, vyhľadať zaujímavé asociácie v správaní užívateľov, či nájsť v bežnom užívateľskom správaní predom neznáme sekvencie., This bachelor thesis deals with the problem of knowledge discovery from web logs. The data source in the form of web access logs allows, after appropriate preprocessing, the use of a number of techniques that are designed to deal with knowledge discovery. By applying these techniques to preprocessed data, it is possible to classify user behavior into groups, to discover interesting associations in user behavior, or to discover previously unknown sequences in common user behavior.

Táto bakalárska práca rieši problematiku získavania znalostí z webových logov. Zdroj dát v podobe webových prístupových logov umožnuje po vhodnom predspracovaní použitie mnohých techník, ktoré sú určené pre získavanie znalostí. Aplikáciou týchto techník na predspracované dáta je možné klasifikovať užívateľské správanie do skupín, vyhľadať zaujímavé asociácie v správaní užívateľov, či nájsť v bežnom užívateľskom správaní predom neznáme sekvencie., This bachelor thesis deals with the problem of knowledge discovery from web logs. The data source in the form of web access logs allows, after appropriate preprocessing, the use of a number of techniques that are designed to deal with knowledge discovery. By applying these techniques to preprocessed data, it is possible to classify user behavior into groups, to discover interesting associations in user behavior, or to discover previously unknown sequences in common user behavior.

Cílem této bakalářské práce je návrh a implementace aplikace umožňující porovnávat výkonnost a časovou náročnost zvolených algoritmů pro dolování frekventovaných množin a asociačních pravidel. Pro demonstraci byly vybrány dolovací algoritmy Apriori, AprioriTIDList, AprioriItemSet a metoda s využitím FP-stromu. Testování probíhalo nad různými objemy dat a s různými hodnotami minimální podpory a spolehlivosti. Aplikace je implementována v objektově orientovaném jazyce C# a jako zdroj dat slouží relační databáze na MS SQL Server 2008., The main goal of this bachelor's thesis is design and implementation of the application that provides a comparison of the performance and time consumption of given algorithms for mining of the frequent itemsets and the association rules. For demonstration, the mining algorithms Apriori, AprioriTIDList, AprioriItemSet and the method using FP-tree were chosen. The tests were executed over various amounts of data and with different minimum support and confidence values as well. The application was implemented in the object oriented language C# and the relational database provided by MS SQL Server 2008 is used as the data source.

Dolování frekventovaných vzorů z databází je již dobře prozkoumanou oblastí. Jak se však ukázalo, tyto algoritmy nejsou příliš vhodné pro zpracování proudu dat. Při dolování frekventovaných množin v proudu dat se musí udržovat kromě jednotlivých množin i jejich historie - a to nejen těch frekventovaných, ale i potenciálně frekventovaných, neboť nefrekventovaná množina se může stát časem frekventovanou. To zvyšuje nároky jak na paměť, tak na výpočetní výkon. Tato práce popisuje dva algoritmy: Lossy Counting a FP-stream. Součástí je také efektivní implementace těchto algoritmů v jazyce C# a jejich porovnání na základě měření., Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunately, these algorithms are not suitable for data stream processing. In frequent-pattern mining from data streams, it is important to manage sets of items and also their history. There are several reasons for this; it is not just the history of frequent items, but also the history of potentially frequent sets that can become frequent later. This requires more memory and computational power. This thesis describes two algorithms: Lossy Counting and FP-stream. An effective implementation of these algorithms in C# is an integral part of this thesis. In addition, the two algorithms have been compared.

Dolování frekventovaných vzorů z databází je již dobře prozkoumanou oblastí. Jak se však ukázalo, tyto algoritmy nejsou příliš vhodné pro zpracování proudu dat. Při dolování frekventovaných množin v proudu dat se musí udržovat kromě jednotlivých množin i jejich historie - a to nejen těch frekventovaných, ale i potenciálně frekventovaných, neboť nefrekventovaná množina se může stát časem frekventovanou. To zvyšuje nároky jak na paměť, tak na výpočetní výkon. Tato práce popisuje dva algoritmy: Lossy Counting a FP-stream. Součástí je také efektivní implementace těchto algoritmů v jazyce C# a jejich porovnání na základě měření., Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunately, these algorithms are not suitable for data stream processing. In frequent-pattern mining from data streams, it is important to manage sets of items and also their history. There are several reasons for this; it is not just the history of frequent items, but also the history of potentially frequent sets that can become frequent later. This requires more memory and computational power. This thesis describes two algorithms: Lossy Counting and FP-stream. An effective implementation of these algorithms in C# is an integral part of this thesis. In addition, the two algorithms have been compared.

Cílem této bakalářské práce je návrh a implementace aplikace umožňující porovnávat výkonnost a časovou náročnost zvolených algoritmů pro dolování frekventovaných množin a asociačních pravidel. Pro demonstraci byly vybrány dolovací algoritmy Apriori, AprioriTIDList, AprioriItemSet a metoda s využitím FP-stromu. Testování probíhalo nad různými objemy dat a s různými hodnotami minimální podpory a spolehlivosti. Aplikace je implementována v objektově orientovaném jazyce C# a jako zdroj dat slouží relační databáze na MS SQL Server 2008., The main goal of this bachelor's thesis is design and implementation of the application that provides a comparison of the performance and time consumption of given algorithms for mining of the frequent itemsets and the association rules. For demonstration, the mining algorithms Apriori, AprioriTIDList, AprioriItemSet and the method using FP-tree were chosen. The tests were executed over various amounts of data and with different minimum support and confidence values as well. The application was implemented in the object oriented language C# and the relational database provided by MS SQL Server 2008 is used as the data source.

Táto bakalárska práca rieši problematiku získavania znalostí z webových logov. Zdroj dát v podobe webových prístupových logov umožnuje po vhodnom predspracovaní použitie mnohých techník, ktoré sú určené pre získavanie znalostí. Aplikáciou týchto techník na predspracované dáta je možné klasifikovať užívateľské správanie do skupín, vyhľadať zaujímavé asociácie v správaní užívateľov, či nájsť v bežnom užívateľskom správaní predom neznáme sekvencie., This bachelor thesis deals with the problem of knowledge discovery from web logs. The data source in the form of web access logs allows, after appropriate preprocessing, the use of a number of techniques that are designed to deal with knowledge discovery. By applying these techniques to preprocessed data, it is possible to classify user behavior into groups, to discover interesting associations in user behavior, or to discover previously unknown sequences in common user behavior.

Cílem této bakalářské práce je návrh a implementace aplikace umožňující porovnávat výkonnost a časovou náročnost zvolených algoritmů pro dolování frekventovaných množin a asociačních pravidel. Pro demonstraci byly vybrány dolovací algoritmy Apriori, AprioriTIDList, AprioriItemSet a metoda s využitím FP-stromu. Testování probíhalo nad různými objemy dat a s různými hodnotami minimální podpory a spolehlivosti. Aplikace je implementována v objektově orientovaném jazyce C# a jako zdroj dat slouží relační databáze na MS SQL Server 2008., The main goal of this bachelor's thesis is design and implementation of the application that provides a comparison of the performance and time consumption of given algorithms for mining of the frequent itemsets and the association rules. For demonstration, the mining algorithms Apriori, AprioriTIDList, AprioriItemSet and the method using FP-tree were chosen. The tests were executed over various amounts of data and with different minimum support and confidence values as well. The application was implemented in the object oriented language C# and the relational database provided by MS SQL Server 2008 is used as the data source.

Dolování frekventovaných vzorů z databází je již dobře prozkoumanou oblastí. Jak se však ukázalo, tyto algoritmy nejsou příliš vhodné pro zpracování proudu dat. Při dolování frekventovaných množin v proudu dat se musí udržovat kromě jednotlivých množin i jejich historie - a to nejen těch frekventovaných, ale i potenciálně frekventovaných, neboť nefrekventovaná množina se může stát časem frekventovanou. To zvyšuje nároky jak na paměť, tak na výpočetní výkon. Tato práce popisuje dva algoritmy: Lossy Counting a FP-stream. Součástí je také efektivní implementace těchto algoritmů v jazyce C# a jejich porovnání na základě měření., Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunately, these algorithms are not suitable for data stream processing. In frequent-pattern mining from data streams, it is important to manage sets of items and also their history. There are several reasons for this; it is not just the history of frequent items, but also the history of potentially frequent sets that can become frequent later. This requires more memory and computational power. This thesis describes two algorithms: Lossy Counting and FP-stream. An effective implementation of these algorithms in C# is an integral part of this thesis. In addition, the two algorithms have been compared.

Dolování frekventovaných vzorů z databází je již dobře prozkoumanou oblastí. Jak se však ukázalo, tyto algoritmy nejsou příliš vhodné pro zpracování proudu dat. Při dolování frekventovaných množin v proudu dat se musí udržovat kromě jednotlivých množin i jejich historie - a to nejen těch frekventovaných, ale i potenciálně frekventovaných, neboť nefrekventovaná množina se může stát časem frekventovanou. To zvyšuje nároky jak na paměť, tak na výpočetní výkon. Tato práce popisuje dva algoritmy: Lossy Counting a FP-stream. Součástí je také efektivní implementace těchto algoritmů v jazyce C# a jejich porovnání na základě měření., Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunately, these algorithms are not suitable for data stream processing. In frequent-pattern mining from data streams, it is important to manage sets of items and also their history. There are several reasons for this; it is not just the history of frequent items, but also the history of potentially frequent sets that can become frequent later. This requires more memory and computational power. This thesis describes two algorithms: Lossy Counting and FP-stream. An effective implementation of these algorithms in C# is an integral part of this thesis. In addition, the two algorithms have been compared.

Táto bakalárska práca rieši problematiku získavania znalostí z webových logov. Zdroj dát v podobe webových prístupových logov umožnuje po vhodnom predspracovaní použitie mnohých techník, ktoré sú určené pre získavanie znalostí. Aplikáciou týchto techník na predspracované dáta je možné klasifikovať užívateľské správanie do skupín, vyhľadať zaujímavé asociácie v správaní užívateľov, či nájsť v bežnom užívateľskom správaní predom neznáme sekvencie., This bachelor thesis deals with the problem of knowledge discovery from web logs. The data source in the form of web access logs allows, after appropriate preprocessing, the use of a number of techniques that are designed to deal with knowledge discovery. By applying these techniques to preprocessed data, it is possible to classify user behavior into groups, to discover interesting associations in user behavior, or to discover previously unknown sequences in common user behavior.