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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]

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 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]

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 feed rate is an important evaluation index of combine harvester performance. The quick identification of the amount of feed rate that enters the combine during harvesting is of great significance for the efficiency and operational quality of the combine harvester. To address this issue, this study proposes a feed rate discrimination method based on association rule mining. A self-designed data acquisition system was designed, taking the wheat combine harvester as object, and collected seven speed signals and three torque signals when the feed rate was 6 kg/s-8 kg/s, 8 kg/s-10 kg/s, and 10 kg/s-11 kg/s, respectively. The collected time series data were discretized so as to facilitate the construction of transaction sets. Then, the association rules in the constructed transaction set were mined by FP-Growth, and the rules with weak or no correlation with the increase in feed rate were filtered using min-support, min-confidence, and min-lift of 1.3, 0.8, and 3, respectively, to obtain strong association rules. Then, the strong association rules were constructed as classifiers. The test results showed that the accuracy of the constructed classifier for the identification of 6 kg/s-8 kg/s, 8 kg/s-10 kg/s, and 10 kg/s-11 kg/s feed rates was 100 %, 96 %, and 98.7 %, respectively. Research results can provide a basis for the adjustment of the working state of the combine harvester. [ABSTRACT FROM AUTHOR]

This study aimed to find associated patterns by association rule mining and propose a prediction model using ensemble learning methods of high levels of trauma items affecting depression and suicide among primary school students in Thai rural extended opportunity schools. Our proposed methods were different from others that have analysed the relationship of high life trauma leading to depression and suicide by using statistical analysis. We found strongly associated patterns and effects among primary students’ trauma, depression, and suicide. The trauma of psychological abuse and neglect may result in suicide, whereas psychological abuse, neglect, and the experience of self-harm are also likely to result in the increased severity of traumatic events in life. The trauma of physical and sexual abuse, neglect, helplessness, feeling worthless, being weak, and self-harm were associated with depression. Our research discovered new knowledge that the risk of suicide arises from two extreme types of trauma: when children’s safety is frequently threatened and the family communicates frequently using rude or abusive words; these traumas may not merely correlate with depression but may ultimately result in suicide. Moreover, this study discovered 7 highly important trauma items and 4 suicide items for predicting depression and suicide using the Random Forest technique. We found that the Random Forest technique performed well in predicting depression and suicide. The predicted depression results show that the overall accuracy was 85.84%, precision was 89.33%, and recall was 75.28%. The predicted suicide results show that the overall accuracy was 91.28%, precision was 89.05%, and recall was 84.72%. From these results, we identified high life trauma affecting depression and suicide, which are very beneficial to practitioners to use in preliminary screening. In addition, those involved need to be aware and attentive in counselling these people with these symptoms in time. Doi: 10.28991/ESJ-2022-06-04-02 Full Text: PDF

The proposed work aims to solve data sparsity problem in the recommendation system. It handles two-level pre-processing techniques to reduce the data size at the item level. Additional resources like items genre, tag, and time are added to learn and analyse the behaviour of the user preferences in-depth. The advantage of the proposed method is to recommend the item, based on user interest pattern and avoid recommending the outdated items. User information are grouped based on similar item genre and tag feature. This effectively handle overlapping conditions that exist on item's genre, as it has more than one genre at initial level. Further, based on time, it analyses the user non-static interest. Overall it reduces the dimensions which is an initial way to prepare data, to analyse hidden pattern. To enhance the performance, the proposed method utilized Apache's spark Mllib FP-Growth and association rule mining approach in a distributed environment. To reduce the computation cost of constructing tree in FP-Growth, the candidate data set is stored in matrix form. The experiments were conducted using MovieLens data set. The observed results shows that the proposed method achieves 4% increase in accuracy when compared to earlier methods. [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 from the 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.

One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa. As a result, some previous association rules may become invalid and some new association rules may emerge. We designed a new, more efficient approach for incremental association rule mining using a Fast Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth), a new Incremental Conditional Pattern tree (ICP-tree), and a compact sub-tree suitable for incremental mining of frequent itemsets. This algorithm retrieves previous frequent itemsets that have already been mined from the original database and their support counts then use them to efficiently mine frequent itemsets from the updated database and ICP-tree, reducing the number of rescans of the original database. Our algorithm reduced usages of resource and time for unnecessary sub-tree construction compared to individual FP- Growth, FUFP-tree maintenance, Pre-FUFP, and FCFPIM algorithms. From the results, at 3% minimum support threshold, the average execution time for pattern growth mining of our algorithm performs 46% faster than FP- Growth, FUFP-tree, Pre-FUFP, and FCFPIM. This approach to incremental association rule mining and our experimental findings may directly benefit designers and developers of computer business intelligence methods.

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]

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]

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]

In this research we extract knowledge from human resources data, accumulated in IT companies for the right selection of teams to work on software projects. We are looking for interesting and unknown dependencies and connections in the data, based on which managers can form more cohesive and professionally working project teams. The proposed approach to improve the selection of teams working on IT projects is based on association rule mining and can be used by IT managers to select the members of the teams. The approbation of the proposed approach is made using the software product RapidMiner. [ABSTRACT FROM AUTHOR]

The paper proposes a new method to mine multiple-level fuzzy association rules among items at a concept hierarchy, using fuzzy partition and FP-growth algorithm. The method primarily consists of two phases: one is to find fuzzy grids at each level and the other is to generate multiple-level fuzzy association rules from those frequent patterns. As demonstrate with realistic database and different thresholds, the experimental results illustrate the proposed method are perceived more efficient. [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]

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]

Automation in pharmacies has achieved innovative levels of effectiveness and savings. In the present day, automated pharmacies are facing extremely large demands of prescription orders specifically at the central fill pharmacies that distribute drugs to retail pharmacies. As a result, improvements are necessary to the Robotic Prescription Dispensing System (RPDS) and RPDS planogram to increase the throughput of prescriptions. RPDS planogram defines where to allocate the dispensers inside the robotic unit and how to distribute them among the multiple robotic units. This research utilizes the pharmacy prescriptions database to extract useful knowledge to improve different strategies in pharmacy automation by using a data mining approach. In this study, a data mining tool is proposed to enhance pharmacy automation. Frequent Pattern Growth (FP-growth) approach, which is one of the algorithms of Association Rule Mining (ARM), is applied to an actual prescriptions database of a central fill pharmacy to study the associations within the prescribed drug regime. The FP-growth application in a prescriptions database is novel; thus, FP-growth is tested on both sequential mode, and parallel mode by using a distributed platform Hadoop and MapReduce paradigm. Two types of association rules are extracted: 1) associations among different drugs that involve their different dosage strengths and manufacturers; and 2) associations that include only information about different drug generic and brand names. The importance of the extracted association rules is evaluated by the use of different measures, including the support, confidence, lift and conviction. The discovered rules disclose strong associations among the purchased drugs that improve the allocation and distribution of dispensers among the robotic units, in addition to enhancements in other pharmacy managerial strategies. [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]

Association rule mining is an important process in the field of data mining, discovering not trivial relationship between a large set of data items. For extracting association rules from an online retail transaction data set we used two of the most known algorithms i.e. FP-Growth and Apriori. Based on the particularities of processes built, we performed a statistical analysis to illustrate the efficiency, precision and accuracy of data mining techniques used. [ABSTRACT FROM AUTHOR]

Most of the existing methods to solve the problem of association rules mining (ARM) rely on special data structures to project the database (either totally or partially) in the primary memory. Traditionally, these data structures reside in the main memory and rely on the existing paging mechanism of the virtual memory manager (VMM) to handle the storage problem when they go out of the primary memory. Typically, VMM stores the overloaded data into the secondary memory based on some preassumed memory usage criteria. However, this direct and unplanned use of virtual memory results in an unpredictable behavior or thrashing, as depicted by some of the works described in the literature. This problem is tackled in this paper by presenting an ARM model capable of mining a transactional database, regardless of its size and without relying on the underlying VMM; the proposed approach could use only a bounded portion of the primary memory and this gives the opportunity to assign other parts of the main memory to other tasks with different priority. In other words, we propose a specialized memory management system which caters to the needs of the ARM model in such a way that the proposed data structure is constructed in the available allocated primary memory first. If at any point the structure grows out of the allocated memory quota, it is forced to be partially saved on secondary memory. The secondary memory version of the structure is accessed in a block-by-block basis so that both the spatial and temporal localities of the I/O access are optimized. Thus, the proposed framework takes control of the virtual memory access and hence manages the required virtual memory in an optimal way to the best benefit of the mining process to be served. Several clever data structures are used to facilitate these optimizations. Our method has the additional advantage that other tasks of different priorities may run concurrently with the main mining task with as little interference as possible because we do not rely on the default paging mechanism of the VMM. The reported test results demonstrate the applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]