Mining Approximate Frequent Itemsets Using Pattern Growth Approach

Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases to obtain complete AFIs. First phase generates core patterns, and second phase examines approximate conditions of core patterns. Specifically, the article proposes novel techniques that how to map transactions on approximate FP-tree, and how to mine AFIs from the conditional patterns of approximate FP-tree. The approximate FP-tree maps transactions on shared branches when the transactions share a similar set of items. This reduces the size of databases and helps to efficiently compute the approximate conditions of candidate itemsets. We compare the performance of our algorithm with the state of the art AFI mining algorithms on benchmark databases. The experiments are analyzed by comparing the processing time of algorithms and scalability of algorithms on varying database size and transaction length. The results show pattern growth approach mines AFIs in less processing time than related Apriori-based algorithms.

FP-Growth Algorithm for Discovering Region-Based Association Rule in the IoT Environment

With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association rules that may appear differently for each region, however, may not be found when the association rules are searched for all data. In this paper, we propose region-based frequent pattern growth (RFP-Growth) to search for association rules by dense regions. First, RFP-Growth divides item transaction included position data into regions by a density-based clustering algorithm. Second, frequent pattern growth (FP-Growth) is performed for each transaction divided by region. The experimental results show that RFP-Growth discovers new association rules that the original FP-Growth cannot find in the whole data.

Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and their Association with Mortality through a Frequent Pattern Growth Association Algorithm. (Preprint)

BACKGROUND Chronic diseases are responsible for most health problems in older people. We know that chronic conditions tend to cluster in the form of patterns, also known as multimorbidity patterns. However, health systems and professionals are generally organized and trained to respond to specific diseases independently, negatively impacting patients and health systems. Different initiatives are trying to respond to these problems. In this context, the current availability of electronic health records and other types of health research data represents an excellent research opportunity. However, there are also some relevant limitations and challenges related to a current lack of tools that allow us to access, harmonize, integrate and reuse datasets technically, legally, ethically, and respectfully to patients and society. In this sense, the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles can help us to guide scientific data management and stewardship and drive scientific discovery to a new paradigm. FAIR4Health is a European Commission supported project that applies FAIR principles on publicly-funded research datasets. OBJECTIVE To present the FAIR4Health pathfinder case study designed to validate and evaluate the FAIR4Health solution with the aim of identifying multimorbidity patterns and their association with mortality in older adults from different health organizations databases of four European countries. METHODS To apply the FAIR principles in five European cohorts from different healthcare settings (i.e., primary care, hospitals, and nursing homes) and institutions (i.e., University of Geneva from Switzerland, Università Cattolica del Sacro Cuore from Italy, University of Porto from Portugal, Instituto Aragonés de Ciencias de la Salud from Spain, and Andalusian Health Service also from Spain), a multicentric retrospective observational study (N = 11,034) was performed. In FAIR4Health, a workflow was designed to implement the FAIR principles on health datasets, and two tools were developed, a Data Curation Tool to transform the raw datasets into FAIR datasets and a Data Privacy Tool to preserve data privacy. On top of these, the FAIR4Health Platform was implemented to provide an interface for researchers, and enable the usage of federated machine learning algorithms on FAIR datasets. In this study, we applied a federated frequent pattern growth association algorithm to identify the most frequent disease patterns among a set of variables. RESULTS We applied the FAIR principles in the health research datasets from different organizations, and we were able to reuse and integrate heterogeneous datasets, increasing the variability of data compared to the studies not applying those principles. We identified and described high-frequent multimorbidity patterns consistent with the literature and observed a strong association with polypharmacy and mortality. CONCLUSIONS Our results highlight the importance of implementing the FAIR data policy to overcome the difficulties in data management and accelerate responsible health research with patients and society.

Achieving effective customer relationship using frequent pattern-growth algorithm association rule learning technique

Customer relationship management (CRM) is a methodology and tool that possesses the plan and techniques that companies should follow in relating with their customers. In today’s business world, the relationship between organizations and their customers is very important in engaging the customers’ interest, which has the direct effect in increasing the business profit. Due to ineffective collaboration and interaction between organizations and their customers, identifying who the real customers are, along with what they need has failed. A breach of trust from the company, and lack of feedback from the customer could make an organization not to be able to compete with her rivals in a business environment and win her customers’ loyalty. Therefore, the guarantee of the customer continuing transactions with the company may no longer be assured. This work deploys an association rule learning technique of data mining using frequent pattern growth algorithm to identify which items are regularly purchased together by customers and based on this result, analyzes and understands the customers’ buying habits. Object-Oriented Analysis and Design methodology (OOAD) is used to analyze and design the system, whereas the implementation is carried out using Python programming language and My-SQL database management system. The contribution of this work is that it enables firms to gather and analyze customers’ interests in a product so that the needs of their valued customers will be met in order to make them return for more business transactions, thereby achieving customer retention.

Rekomendasi Paket Menu Angkringan Waru Tanjung Bias Dengan Algoritma Frequent Pattern Growth Berbasis Web

Culinary business using carts selling various kinds of heavy food, light and drinks, is favored by many people to just fill their stomachs, gather with friends and even family. Culinary businesses or culinary destinations like this are known as Angkringan which are increasingly mushrooming in the millennial generation. Angkringan Waru, located in Tanjung Bias, is a gathering destination for all people to enjoy a relaxed atmosphere on the beach. Angkringan Waru provides 85 types of menus for its customers, the many menus often confuse customers in choosing snacks while enjoying the beachside atmosphere. Starting from these problems, data mining techniques are used with the Frequent Pattern Growth (Fp-Growth) algorithm to recommend items in producing a menu package consisting of 1 snack item and 1 drink item. The dataset used is transaction data from Angkringan Waru as many as 870 transactions, the resulting output is a menu package recommendation rule and implemented in a web for Angkringan Waru. The Fp-Growth Data Mining Application by providing a minimum support value of 20% and Confident 50% with a lift ratio > 1 produces 57 rules or menu package recommendations that will be offered to Angkringan Waru customers. The results of the application in the form of 57 menu package recommendations are then used as recommendations for Angkringan Waru customers, where these menus are the favorite menus of customers at Angkringan Waru.

Marketing Strategy Using Frequent Pattern Growth

The biggest problem faced by printing companies during the Covid-19 pandemic was that the number of orders was unstable and tends to decrease, which had the potential to harm the company. Therefore, various appropriate marketing strategies were needed so that the number of product orders was relatively stable and even increases. The impact was that the company could survive and continued to grow. This study aimed to assist company managers in developing appropriate marketing strategies based on association rules generated from one of the data mining methods, namely the Frequent Pattern Growth (FP-Growth) method. The case study of this research was a printing company where there was no similar research that used a printing company's dataset. This study produced nine association rules that meet a minimum of 25% support and a minimum of 60% confidence, but only two association rules that had a high positive correlation, namely for a custom paper bag and banner products. Therefore, several marketing strategies were suggested that could be used as guidelines for companies in managing sales packages and giving special discounts on a product. The results of this study are expected to trigger an increase in the number of product orders because this study tried to find the right product for consumers and did not try to find the right consumers for a product.

A Graph Mining Approach for Ranking and Discovering the Interesting Frequent Subgraph Patterns

Graph mining is a well-established research field, and lately it has drawn in considerable research communities. It allows to process, analyze, and discover significant knowledge from graph data. In graph mining, one of the most challenging tasks is frequent subgraph mining (FSM). FSM consists of applying the data mining algorithms to extract interesting, unexpected, and useful graph patterns from the graphs. FSM has been applied to many domains, such as graphical data management and knowledge discovery, social network analysis, bioinformatics, and security. In this context, a large number of techniques have been suggested to deal with the graph data. These techniques can be classed into two primary categories: (i) a priori-based FSM approaches and (ii) pattern growth-based FSM approaches. In both of these categories, an extensive research work is available. However, FSM approaches are facing some challenges, including enormous numbers of frequent subgraph patterns (FSPs); no suitable mechanism for applying ranking at the appropriate level during the discovery process of the FSPs; extraction of repetitive and duplicate FSPs; user involvement in supplying the support threshold value; large number of subgraph candidate generation. Thus, the aim of this research is to make do with the challenges of enormous FSPs, avoid duplicate discovery of FSPs, and use the ranking for such patterns. Therefore, to address these challenges a new FSM framework A RAnked Frequent pattern-growth Framework (A-RAFF) is suggested. Consequently, A-RAFF provides an efficacious answer to these challenges through the initiation of a new ranking measure called FSP-Rank. The proposed ranking measure FSP-Rank effectively reduced the duplicate and enormous frequent patterns. The effectiveness of the techniques proposed in this study is validated by extensive experimental analysis using different benchmark and synthetic graph datasets. Our experiments have consistently demonstrated the promising empirical results, thus confirming the superiority and practical feasibility of the proposed FSM framework.

Market Basket Analysis untuk Swalayan KSU Sumber Makmur dengan Algoritma FP Growth

Salah satu teknik data mining yang populer digunakan adalah association data mining atau yang biasa disebut dengan istilah market basket analysis. Market basket didefinisikan sebagai suatu itemset yang dibeli secara bersamaan oleh pelanggan dalam suatu transaksi. Market basket analysis adalah suatu sarana untuk meningkatkan penjualan. Metode ini dimulai dengan mencari sejumlah frequent itemset dan dilanjutkan dengan pembentukan aturan-aturan asosiasi. Algoritma Apriori dan frequent pattern growth adalah dua algoritma yang sangat populer untuk menemukan sejumlah frequent itemset dari data-data transaksi yang tersimpan dalam basis data. Dalam penelitian ini algoritma frequent pattern growth (FP Growth) digunakan untuk menemukan sejumlah aturan asosiasi dari basis data transaksi penjualan di Swalayan KSU Sumber Makmur (Trenggalek). Dari hasil pengolahan data didapatkan pola pembelian paling kuat berupa jika membeli pasta gigi maka dimungkinkan juga akan membeli sabun dan jika membeli shampo juga akan membeli sabun dengan tingkat keyakinan (confidence) 63% dan 62%.