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Data is increasingly recognized as a valuable asset for generating new insights and information. Given the importance of data, businesses must always look for ways to get more value from data generated from sales transactions. In data mining, association rule mining is a good standard technique and is widely used to find interesting relationships in databases. Association rule is closely related to market basket analysis to find items that often appear together in one transaction. This study proposes the frequent pattern growth (FP-Growth) algorithm in finding association rules on sales transaction data. Our methodology includes dataset preparation for modeling, evaluation of model performance, and subsequent integration into a web-based platform. We conducted a comparative analysis of the FP-Growth algorithm against the Apriori algorithm, finding that FP-Growth outperformed Apriori in efficiency. Using the same dataset and constraint level, both algorithms produce the same number of frequent itemsets. However, in terms of computation time, FP-Growth excels by taking 2.89 seconds while Apriori takes 5.29 seconds. We integrated trained FP-Growth algorithm into a web-based dashboard application using the streamlit framework. This system is anticipated to simplify the process for businesses to identify customer purchasing patterns and improve sales. [ABSTRACT FROM AUTHOR]

In this modern era, there is so much competition in the business world, especially in the sales industry, that requires shop owners to find a strategy that can increase sales and marketing of the products they sell, one of which is by utilizing clothing sales transaction data using data mining. Data Mining is an iterative and interactive process to find new patterns or models that can be generalized for the future, useful and understandable in a very large database (massive database). In these conditions, good data processing techniques are needed, one of which is data mining techniques. One thing that can be used in this technique is to use the Association rule method using the FP-Growth algorithm, which is an algorithm that produces frequent itemsets which will later be used to determine recommendations for clothing products for the needs of the Siny.co Store. This research uses the RapidMiner Studio application to help process transaction data. This research method uses a minimum support of 20% and a minimum confidence of 80%, thereby creating 9 association rules, where these rules determine several products for recommendation at the Siny.Co Store, including Sashi, Canna, Alice, Tartan, Aruna, Cassandra, Nala, Lalita, and Acio. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to make full use of the accumulated curriculum data of Folk costume and dig out useful information from it, so as to provide useful information for curriculum teaching, the article proposes three general functions based on the requirement analysis, and pre-processes the completed grade data of ethnic costume students in 4 academic years, analyzes these data by FP-growth algorithm to understand the situation of association rules between different courses, and through K-means++ algorithm The clustering analysis of students with different levels of achievement is carried out and the results are validated by examples. In the algorithm performance analysis, the performance of FP-growth algorithm is better, the average absolute error of FP-growth algorithm is always smaller than that of Apriori algorithm; When the support degree is 20%, the running time of FP-growth algorithm is 0.4s, which is 0.4s less than that of Apriori algorithm. when the number of calculation nodes is 5, the running time of FP-growth algorithm and the accuracy of the K-means++ algorithm were higher than that of the K-means algorithm. In the Iris dataset, the accuracy of the K-means++ algorithm was 91.05%, which was 8.94% higher than that of the K-means algorithm. When mining the course grade data, the confidence level of the obtained association rules was even higher, even up to 97.15%. The standardized test score for the second group of students was 0.960. The course evaluation method used in the article was more objective and the accuracy of the data analysis was higher, providing valuable reference information for teachers' teaching. [ABSTRACT FROM AUTHOR]

The limited ability to memorize, understand Arabic, and access sources are obstacles to understanding Islamic knowledge, especially understanding hadith. Searching for access to sources to obtain hadiths that are in accordance with the theme and the interrelationships between themes improves the quality of the preachers towards religious understanding. This study investigates how to automate the thematic tracking pattern of Prophet Muhammad's hadith using the FP-Growth (Frequent Pattern-Growth) and ECLAT algorithms. The research was conducted using qualitative and quantitative approaches. The next step involves creating a prototype using the dataset development framework. The result of this study is a prototype of hadith tracing using the FP-Growth algorithm with the prophet's hadith dataset based on the Bulughul Maram book. [ABSTRACT FROM AUTHOR]

The aim of this study is to identify crypto assets with similar characteristics and to explore the similar responses of these assets to market-priced events. This process is carried out in two stages. Cluster analysis and association analysis were applied in the research. First of all, cluster analysis was performed using the variables; the total number of active unique addresses, USD value of the current supply, fixed closing price of the asset, return on investment of the asset, total of the current supply, number of transactions, USD value of the sum of native units and 30 days volatility criteria. HK-Means algorithm and R Program were used for clustering. Then, the co-movement of crypto assets was analyzed using the FP-Growth algorithm and the WEKA program. 71 crypto assets with the highest market capitalization and meeting the research criteria were included in the research. The data used in the research covers the period of May 2021-May 2022. According to the main findings obtained from the research; within the framework of the criteria used in the research, 4 clusters were formed. Most important association rules found to be between; btc (bitcoin) & aave (nominex), eth (ethereum) & aave (nominex), dot (polkadot) & aave (nominex), neo & aave (nominex), uni (uniswap) & aave (nominex), btg (bitcoin gold) & etc (ethereum classic), xrp (riple) & algo (algorand) & doge (dogecoin), xrp (riple) & doge (dogecoin), cro (cronos) & xrp (riple) & algo ( algorand) & trx (tron) & doge (dogecoin). [ABSTRACT FROM AUTHOR]

In order to explore potential value of explosively growing data in thermal power unit, this paper proposes a big data mining method based on Hadoop-based Spark cluster-computing framework and algorithms. Firstly, positive and negative balance methods are used to accurately obtain actual net coal consumption, and maximum information coefficient method is used to select all parameters related to optimization objectives. Then, Spark-based Mini-Batch K-means algorithm and Elbow method are constructed to divide whole operating modes. After that, all data are discretized and mapped to corresponding intervals by using Spark-based Elbow method and Mini-Batch K-means algorithm. Finally, Spark-based parallel FP-growth algorithm is used to deeply mine the potential relationships and laws. To verify the proposed method, a 350-MW thermal power unit is taken as a study case. The important conclusions are as follows: (1) the proposed Spark-based Mini-Batch K-means algorithm reduces the calculation time by 57.11% compared with Mini-Batch K-means algorithm, and 85.61% calculation time compared with K-means algorithm. The proposed Spark-based FP-growth algorithm reduces computational time by 32.8% compared with FP-growth algorithm. (2) Strong association rules of whole operating modes are mined, and operating optimization guidance schemes for important parameters are obtained. Take operating mode 1 as an example: if the optimal result can be reasonably applied, it can save 2.942 g coal per kilowatt hour. (3) Besides, we have found out some other potential relationships among parameters, which have important reference value for on-site operators to analyze economy of the thermal power unit. [ABSTRACT FROM AUTHOR]

By launching a war against Ukraine, Russia changed the entire world system and demonstrated the ineffectiveness of the global security system. Today it is necessary to look for effective solutions to support the adoption of security decisions and develop effective strategies for the formation of a new architecture of the international security system. The paper aims to obtain preliminary approximate estimates of the state commitments of the countries of the world that support Ukraine in the war with Russia and to reveal non-obvious connections and regularities in the provision of various types of aid. The data were collected on the rough refugee cost estimate, quantitative assessment of the government-to-government commitments, and preliminary data on non-bilateral aid transferred by governments to Ukraine of 40 countries. We used the FP-Growth algorithm to identify non-obvious connections and patterns between different types of support for Ukraine. We created an associative rules model to detect non-obvious patterns and relationships between the different types of bilateral commitments of the countries, that support Ukraine. Preliminary estimates were obtained between various types of international support for Ukraine in the war against Russia covering February 24 to November 20, 2022, the number of Ukrainian refugees accepted by the respective countries, and the sale of arms to Russia by some of them after the ban. [ABSTRACT FROM AUTHOR]

Under the background of large-scale PV (photovoltaic) integration, generating typical operation scenarios of power systems is of great significance for studying system planning operation and electricity markets. Since the uncertainty of PV output and system load is driven by weather factors to some extent, using PV output, system load, and weather data can allow constructing scenarios more accurately. In this study, we used a TimeGAN (time-series generative adversarial network) based on LSTM (long short-term memory) to generate PV output, system load, and weather data. After classifying the generated data using the k-means algorithm, we associated PV output scenarios and load scenarios using the FP-growth algorithm (an association rule mining algorithm), which effectively generated typical scenarios with weather correlations. In this case study, it can be seen that TimeGAN, unlike other GANs, could capture the temporal features of time-series data and performed better than the other examined GANs. The finally generated typical scenario sets also showed interpretable weather correlations. [ABSTRACT FROM AUTHOR]

For decades, changes in fish catch composition and the marine environment have been monitored worldwide and recorded in databases like FAO FishStatJ and the European Union Copernicus Marine Service. However, the complexity and high variability in the dataset makes it challenging to find meaningful information through conventional data analytical methods. Therefore, in this pilot data mining study, we employed association rule mining algorithms (Apriori, ECLAT, and FP-Growth) to find frequently occurring itemsets in the fish-catch composition and marine environment data of the west and east coasts of India during the past decade (2011-2020). Firstly, the inherent spatial and temporal variations in fish-catch composition and marine environment (sea surface temperature and chlorophyll) on the west and east coasts of India were statistically analyzed and described. Then, the data were preprocessed, selected, and transformed into categorical attributes. By applying the association rule mining algorithms written in the Python language in the Google Colab workspace, we obtained frequent itemsets of fish catch and marine environment with different levels of minimum support and confidence. The preliminary results showed linear and inverse associations between changes in the sea surface temperature, chlorophyll concentration, and major catch groups, such as anchovies, Indian oil sardine, Indian mackerel, hairtails, butterfish-pomfrets, Bombay duck, flatfish, tunas, giant tiger prawn, crabs, lobsters, and cephalopods. Among the tested data mining algorithms, FP-Growth was found to be more efficient and reliable in finding associations between the spatiotemporal dynamics of the marine environment and fish distribution and abundance. Therefore, it can be potentially used to support marine fisheries' resource assessment and management strategies after refinement. [ABSTRACT FROM AUTHOR]

The efficient discovery of frequent itemsets from a transaction database is the fundamental step for association rule mining in data analytics. Interesting associations among the items present in a transaction database contribute to knowledge enrichment. Thus, decision-making and pattern generation from the massive amounts of data become effortless. But one of the major problems associated with the algorithms of frequent itemset mining is excessive memory requirements, which cause them to be inappropriate for larger datasets with itemsets having high cardinality. A few novel data structures for mining frequent itemsets have been introduced in recent years. For example, N-List, NodeSet, DiffNodeSet, proximity list, etc. have been proposed that show a coherent mining approach for improving the execution time while still leaving the scope for further improvements in memory requirements. In this paper, we propose a novel algorithm using cellular learning automata (CLA) and multiple FP tree structures for frequent itemset mining that is efficient in both time and memory requirements. Extensive experimentation has been performed by comparing the performance of the proposed method with the leading algorithms and using publicly available real and synthetic datasets designed specifically for pattern mining algorithms. It can be concluded that the proposed method is memory-efficient and shows comparable execution time with varying dataset dimensions and dataset density, assuring its robustness. In addition to the proposal of the new methodology for frequent itemset mining, its potential domain-specific usage in species biodiversity data analysis has also been discussed. The fact that which groups of species are closely related can be derived from huge occurrence records of species datasets. This could help in understanding species co-occurrence in multiple sites, which in turn assists in solving ecology-related issues for afforesting and reforesting. It could be a step forward toward the advantageous use of computer science in the biodiversity domain. [ABSTRACT FROM AUTHOR]

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Schools and universities shuttered as a result of the worldwide COVID-19 pandemic lockdown, and student screen time skyrocketed. Since the programs are delivered online, a spike in social media use during lockdown resulted in many pupils becoming victims of cyberbullying, which includes criticizing one another, posting sexual comments on images of young ladies, and using fake accounts to bully others. Machine Learning (ML) and Natural Language Processing (NLP) techniques are being used in a growing body of work on automated cyberbullying detection. Different machine learning methods, however, are unable to converge to the requisite accuracy. Thus, numerous classifier systems known as "ensemble learning" are proposed in order to improve predictive performance by aggregating the predictions from various models. In our proposed system, we use a novel method of detecting online harassment (cyberbullying) on the Instagram dataset. The attributes of abusive words are initially analyzed from feature selection and pre-trained word embedding language models like Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMO). A knowledge-based frequent pattern method is used to find the intention of the harasser and is created by the Knowledge-BERT (K-BERT). The unsupervised approaches such as Latent Semantic Analysis (LSA), Frequent pattern growth (FP-Growth), and a clustering technique K-Means. The results from the detection models are ensembled using Extreme Gradient Boosting (XGBoost) for classifying the categories of online harassment. The performance of the ensemble model is then cross-validated using machine learning metrics and compared with various existing techniques. An ensemble model performs better with a higher F1 score of 92.04% with less error rate in the classification of harassment categories. [ABSTRACT FROM AUTHOR]

The safety and stability of power networks have become increasingly important in the field of industrial control. Traditional information analysis for power networks overly relies on expert knowledge, and existing analysis models suffer from problems such as high algorithm complexity and rule redundancy. To address these issues, this paper proposes an advanced alarm information correlation analysis method that takes into account the characteristics of power networks. The method first eliminates noisy parts in the original alarm logs through a pre-processing module, then generates alarm transaction sets using a proposed method based on dynamic sliding time window, and subsequently applies the FP-Growth algorithm to mine alarm association rules for power networks. Finally, a time-based alarm rule filtering algorithm is proposed to eliminate invalid rules. Experiments conducted on alarm data collected from a situation awareness platform deployed in a power grid company show that this method reduces the redundancy of alarm rules by an average of about 30% compared to other similar association analysis method, and can effectively extract key alarm rules in power networks to guide fault warning. [ABSTRACT FROM AUTHOR]

The Apriori algorithm is one of the most widely developed and used association rule algorithms because it can produce optimal rules. The association rule a priori algorithm has three main problems: the old dataset scan process, less than optimal rules formed, and the use of large memory. Therefore, this study aims to improve the performance of the a priori algorithm in the itemset frequency search process with optimal rules, small memory usage, and fast dataset scans. The method used in this study is TID-List Vertical and data partitioning. This study result indicates that the TPQ-Apriori method improves the performance of the dataset scan process up to two times faster in scanning datasets compared to the study's results using the traditional a priori method. The performance of the dataset scan process from the results of this study is also superior in processing speed by up to 7% on scanning datasets compared to the ETDPC-Apriori method on testing three data sets (mushroom, chess, and c20d10k). [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Nanchang University (Natural Science) is the property of Journal of Nanchang University (Natural Science) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Efficient methods of analysis readily available for clinicians continue to be limited within neuropsychological assessment at the raw data level. Here, a novel approach for generating predictive response patterns and analysing neuropsychological raw data is offered. In order to assess the usefulness of association rule learning as an analysis tool for neuropsychological raw data, Frequent Pattern Growth (FP‐Growth) was used to mine patterns from the Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Battery (CERAD‐NB) database. Complete assessment data for 84 post‐mortem confirmed Alzheimer's disease (AD) cases and 294 age, race, and education matched controls were analysed across baseline and one‐year follow‐up using FP‐Growth, for the purpose of assessing the clinical utility of a finer analysis at the raw data level and the feasibility of predicting response patterns for clinical/control groups. Output from FP‐Growth, in terms of the number of frequent itemsets retained across both evaluation timepoints, was discernable between controls, mild, and moderate to severe Alzheimer's disease cases (p <.001, and η2 =.488). Patterns within raw data scores, both in terms of frequent itemsets and predictive association rules, appear to be differentiable across groups within neuropsychological analysis, which indicates that FP‐Growth is applicable as a supplementary analysis tool for neuropsychological assessment by means of offering an additional level of data analysis, predictive item response capabilities, and aiding in clinical decision making. [ABSTRACT FROM AUTHOR]

The Fuzzy Inference System (FIS) is frequently utilized in a variety of Text Mining applications. In the text processing domains, where the amount of the processed data is vast, inserting manual rules for FIS remains a real issue, especially in the text processing domains, where the size of the processed databases is enormous. Therefore, an automated and optimal inference rules (IR) selection strengthens the FIS process. In this work, we propose to apply the FP-Growth as an association model algorithm and an automatic way to identify IR for fuzzy text vectorization. Once the fuzzy vectors are generated, we call the selection variables algorithms, e.g., Info Gain and Relief, to reduce the given descriptor dimensionality. To test the new descriptor performance, we propose multi-classes text classification systems using several machine learning algorithms. Applying benchmarked databases, the new technique to produce Fuzzy descriptors achieves a significant gain in time, precision rules, and weighting quality. Moreover, comparing the classification systems, the accuracy is improved by 10% comparing with other approaches. [ABSTRACT FROM AUTHOR]

Technology prediction is an important technique to help new energy vehicle (NEV) firms keep market advantage and sustainable development. Under fierce competition in the new energy industry, there is an urgent necessity for innovative technology prediction method to effectively identify core and frontier technologies for NEV firms. Among the various methods of technology prediction, one of the most frequently used methods is to make technology prediction from patent data. This paper synthesizes the frequent pattern growth (FP-growth) algorithm and input-output analysis to construct a new technology prediction method based on the knowledge flow perspective, takes the data of NEV patent family in 1989–2018 the Derwent patent database as a sample, divides the data according to the 5-year standard, and uses the method to identify the core and frontier technologies in the NEV field during different periods. Furthermore, the multiple co-occurrence method applies to analyze the technology layout and evolution patterns in China's NEV field. The results show that the technology prediction method proposed in this paper can effectively identify core and frontier technologies to achieve NEV technology prediction. [ABSTRACT FROM AUTHOR]

The automated fault detection and diagnostics (AFDD) of heating, ventilation, and air conditioning (HVAC) using data mining and machine learning models have recently received substantial attention from researchers and practitioners. Various models have been developed over the years for AFDD of complete HVAC or its sub-systems. However, HVAC complexities, which partly have roots in its close coupling nature and interrelated dependencies, mean that understanding the relationship between faults and the suitability of the techniques remains an unanswered question. The literature analysis and interactive visualization of the data collected fromthe past implementation of AFDD models can provide useful insight to further explore this question by applying artificial intelligence (AI). Association rule mining (ARM) is deployed by this paper, using the frequent pattern (FP) growth algorithm to generate frequent fault sets for most common HVAC faults from the body of AFDD models developed in the literature to represent the status quo. A new model is developed for common HVAC faults and the techniques most frequently used to detect and diagnose them. A recommender system is developed using the ARM model to extract knowledge from the body of knowledge of HVAC data-driven AFDD in the form of rule-sets that reflect the associations. Findings of this review paper can significantly help civil and building engineers, as well as facility managers, in better management of building HVAC systems. [ABSTRACT FROM AUTHOR]

The SPADE (spatio-temporal S pike PA ttern D etection and E valuation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85–90% of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE , which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude. [ABSTRACT FROM AUTHOR]

Formal concept analysis (FCA) is an unsupervised machine learning technique used for knowledge discovery and representation. A major task in FCA is the enumeration of the implications to construct the implication base. Even though there are many efficient classical and parallel algorithms proposed for constructing the implication base, the existing algorithms are not well suited for large formal contexts because of their architectural complexity. All the existing works use either stem-base or proper-premise approach to find the implication base in exponential time. Hence, we introduce a distributed algorithm to find the implication base quickly in larger datasets in polynomial time. In this paper, we propose a scalable algorithm to find the implication base using machine learning technique FP-growth, big data processing framework Apache Spark and executed on large formal contexts. Extensive experiments on the real-world datasets show that the proposed algorithm has an improved gain in performance metrics such as execution time, CPU and memory usage. The statistical validations on the experimental results prove that the proposed algorithm has the better potential to find the implication base. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Financial Researches & Studies / Finansal Araştirmalar ve Çalişmalar Dergisi is the property of Marmara University, School of Banking & Insurance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Association of Arab Universities Journal of Engineering Sciences (JAARU) is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mining frequent item sets from transaction datasets is always being one of the most important problems of data mining. Apriori is the most popular and simplest algorithm for frequent itemset mining. Transaction data is a set of recording data resulting in connections with sales-purchase activities at a particular online store. In recent years transaction data has been used in research for discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. This paper focuses on the performance comparison of Apriori and FP-growth algorithms with respect to the execution time. [ABSTRACT FROM AUTHOR]

User Identity Linkage (UIL) across social networks refers to the recognition of the accounts belonging to the same individual among multiple social network platforms. Due to the network user's identities have the characteristics of various sources and real identity cannot be confirmed, it is very easy to become the main means of malicious user to carry out network attacks and spread rumors. User Identity Linkage not only can make the service provider to understand the user and thus to provide better service to the user, but also plays a significant role in improving the ability to find and track malicious users. For the credibility problem on the associated clues of user identification resulted from dynamic IP, shared Internet access and other factors, a user identity linkage method based on user online habit is proposed. This method assumes that the people use multiple network services crosswise when using the internet, converts the association analysis problem of user identification to the frequent pattern mining problem, and performs the optimization from three respective aspects: the online transaction database construction, the fast algorithm for mining frequent patterns and frequent cooccurrence identities consolidation. In order to improve the efficiency of frequent pattern mining, a parallelization of FPGrowth algorithm called MRFP-Growth algorithm is proposed to mine the user identifications of frequent cooccurrence quickly and efficiently. Experiments show that this method can associate multiple accounts of a user in network traffic with more than 85% accuracy in the scenario of dynamic variable IP address with only IP address and online time. [ABSTRACT FROM AUTHOR]

Copyright of International Periodical of Recent Technologies in Applied Engineering is the property of Marmara University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data mining is an important technology to reveal the patterns from crime data. Although there are many researches about this topic, less work models the relations between rates of different kinds of crime. In this paper, an algorithm based on fuzzy association rules (AR) mining is proposed to discover these relations. Two datasets, which are crimes in Chicago from 2012 to 2017 and crimes in NSW from 2008 to 2012, are used for case studies. At first, crime data is preprocessed, where every kind of crime occurring in every district during every month is counted. For a crime in a combination of district and month, the membership function, which is based on hypothesis testing, is designed to evaluate the degree to which its rate is high, normal or low, and the fuzzy transactional dataset is formed. A bridge between fuzzy transactional dataset and binary AR mining algorithm is built, so those mature tools of binary AR mining can be applied to generate fuzzy ARs. In the results of case studies, the strong relations between rates of different crime can be found. There are many interesting and surprise rules, which are worthy to be further studied by domain experts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hospitality & Tourism Technology is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern algorithms for mining frequent itemsets face the noteworthy deterioration of performance when minimum support tends to decrease, especially for sparse datasets. Long-tailed itemsets, frequent itemsets found at lower minimum support, are significant for present-day applications such as recommender systems. In this study, a novel power set based method named as HARnessing the Power of Power sets (HARPP) for mining frequent itemsets is developed. HARPP is based on the concept of power set from set theory and incorporates efficient data structures for mining. Without storing it entirely in memory, HARPP scans the dataset only once and mines frequent itemsets on the fly. In contrast to state-of-the-art, the efficiency of HARPP increases with a decrease in minimum support that makes it a viable technique for mining long-tailed itemsets. A performance study shows that HARPP is efficient and scalable. It is faster up to two orders of magnitude than FP-Growth algorithm at lower minimum support, particularly when datasets are sparse. HARPP memory consumption is less than that of state-of-the-art by an order of magnitude, on most datasets. [ABSTRACT FROM AUTHOR]

In order to recommend an efficient drawing inspecting expert combination, an expert combination is selected by an expert recommendation algorithm based on Pearson's correlation coefficient and FP-growth. By introducing the Pearson correlation coefficient and the FP-growth association rule algorithm, the expert recommendation algorithm can accurately select the participating experts in the historical project similar to the scale of the project to be reviewed, and combine the experts to calculate and obtain the expert group with the highest fit, namely, the expert combination of project to be reviewed. This expert recommendation algorithm based on Pearson correlation coefficient and FP-growth can effectively recommend a kind of expert group with the highest efficiency of collaborative review, which solves the problem of how to recommend efficient expert combination accurately for drawing inspecting system. [ABSTRACT FROM AUTHOR]

According to the complexity of ocean data, this paper adopts a parallel mining algorithm of association rules to explore the correlation and regularity of oxygen, temperature, phosphate, nitrate and silicate in the ocean. After the marine data is interpolated, this paper utilizes the parallel FP-growth algorithm to mine the data and then briefly analyzes the mining results of the frequent itemsets and association rules. The relationship between the parallel efficiency and the core number of CPU is analyzed through datasets with different scales. The experimental results indicate that the acceleration effect is ideal when each thread scored 200,000-300,000 data, which leads to more than 1.2 times of performance improvement. [ABSTRACT FROM AUTHOR]

Odoo is an enterprise resource planning information system providing modules to support the basic business function in companies. This research will look into the development of an additional module at Odoo. This module is a data mining module using Market Basket Analysis (MBA) using FP-Growth algorithm in managing OLTP of sales transaction to be useful information for users to improve the analysis of company business strategy. The FP-Growth algorithm used in the application was able to produce multidimensional association rules. The company will know more about their sales and customers' buying habits. Performing sales trend analysis will give a valuable insight into the inner-workings of the business. The testing of the module is using the data from X Supermarket. The final result of this module is generated from a data mining process in the form of association rule. The rule is presented in narrative and graphical form to be understood easier. [ABSTRACT FROM AUTHOR]

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The increasing E-tourism systems provide intelligent tour recommendation for tourists. In this sense, recommender system can make personalized suggestions and provide satisfied information associated with their tour cycle. Data mining is a proper tool that extracting potential information from large database for making strategic decisions. In the study, association rule analysis based on FP-growth algorithm is applied to find the association relationship among scenic spots in different cities as tour route recommendation. In order to figure out valuable rules, Kulczynski interestingness measure is adopted and imbalance ratio is computed. The proposed scheme was evaluated on Wangluzhe cultural tourism service network operation platform (WCTSNOP), where it could verify that it is able to quick recommend tour route and to rapidly enhance the recommendation quality. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Association rule data mining is an important technique for finding important relationships in large datasets. Several frequent itemsets mining techniques have been proposed using a prefix-tree structure, FP-tree, a compressed data structure for database representation. The DIFFset data structure has also been shown to significantly reduce the run time and memory utilization of some data mining algorithms. Experimental results have demonstrated the efficiency of the two data structures in frequent itemsets mining. This work proposes FDM, a new algorithm based on FP-tree and DIFFset data structures for efficiently discovering frequent patterns in data. FDM can adapt its characteristics to efficiently mine long and short patterns from both dense and sparse datasets. Several optimization techniques are also outlined to increase the efficiency of FDM. An evaluation of FDM against three frequent itemset data mining algorithms, dEclat, FP-growth, and FDM* (FDM without optimization), was performed using datasets having both long and short frequent patterns. The experimental results show significant improvement in performance compared to the FP-growth, dEclat, and FDM* algorithms. [ABSTRACT FROM AUTHOR]

With the booming of social media and health informatics, there exists a pressing need for a powerful tool to sustain comprehensive analysis of public and personal health information. In particular, it should be able (1) to maximize the discovery of association rules amongst data items and (2) to handle the rapid growing data scale. The FP-Growth algorithm is a salient association rule learning method in exploring potential relation in database possibly with a lack of priori knowledge. It has the merits of low time & space complexity, whereas it cannot handle negative association rules which is necessary in comprehensive mining of health data. In order to enable comprehensive discovery of association rules, this study extends the FP-Growth algorithm to mine both positive and negative frequent patterns, namely the PNFP-Growth framework. The extended approach also adopts a prune strategy to filter out misleading patterns to the most by correlating the negative data items and the positive ones. Experiments had been performed to evaluate the performance of the PNFP-Growth over a public data set and a database consisting of thousands of people's real health examination information (collected within 5 years from the date of this publication). The results indicate that (1) the PNFP-Growth can excavate more patterns than the traditional counterpart does while it is still highly efficient, and (2) the analysis upon the health examination data is informative and well complies with the clinical practices, e.g., more than 30 % people suffering from hypertension are having high systolic pressure and liver problems. [ABSTRACT FROM AUTHOR]

FP-Growth algorithm is an algorithm of association rules that does not generate a set of candidate, so it has very high practical value in face of the rapid growth of data volume in wisdom medical treatment. Because FP-Growth is a memory-resident algorithm, it will appear to be powerless when it is used for massive data sets. The paper combines Hadoop and FP-Growth algorithm and through the actual analysis of traditional Chinese medicine (TCM) data compares the performance in two different environments of stand-alone and distributed. The experimental results show that FP-Growth algorithm has a great advantage in the processing and execution of massive data after the MapReduce parallel model, so that it will have better development prospects for intelligent medical treatment. [ABSTRACT FROM AUTHOR]

Data mining field concentrates on theories, concepts, methodologies and mainly on extraction of useful knowledge from large amounts of data for decision making. During their day to day activities healthcare industry generates large amounts of healthcare information that has not been efficiently used to extract unknown information. Therefore the discovery of interesting and useful information usually remains hidden. Diabetes is a healthcare problem and is increasing at a greater rate with every passing year. If not recognized early, can lead to severe health problems, even in organ failures. Several data mining techniques like clustering, classification, association rule mining are used to identify early symptoms of the diseases and stopping them getting to a chronic level. In this paper, an efficient approach has been designed for prediction of risk of getting diabetes using diabetes database. The approach in this paper used more than one data mining techniques showing enhanced result in disease prediction. The data for diabetes is collected and processed to facilitate the mining process. Firstly, the preprocessed database is mined to extract frequent patterns related to diabetes using FP-Growth algorithm. After that ID3 algorithm approach has been used as the training algorithm to depict the risk of diabetes using a Decision Tree. [ABSTRACT FROM AUTHOR]

In this paper, we present a sparse memory allocation data structure for sequential and parallel data mining. We explored three algorithms utilizing the proposed data structure: MASP-tree, apriori-TID, and FP-growth. We modified the data structure of apriori-TID and FP-growth algorithms to reduce memory allocation cost. Five data sets are used for comparison. The results show that the modified apriori-TID has a higher speed-up than the modified FP-growth when the proposed data structure is used. A maximum speed-up of 3.42 is observed when MASP algorithm is tested. [ABSTRACT FROM AUTHOR]

The objective of this study is to apply data mining techniques to determine factors that are commonly associated with liver cancer incidence, using an anonymized data set of 6064 patients from the British Columbia Cancer Agency (BCCA). The association rules indicate that in BC the patient demographic factors associated with increased liver cancer include: age ranges 60-69, male gender, and geographic location in the Greater Vancouver area. The main factors associated with decreased survivability in BC were being male and in the age range 70-79. In the Yukon, being male and in the age range 60-69 was the main factor associated with both increased incidence of liver cancer and decreased survivability. [ABSTRACT FROM AUTHOR]