Mining regular closed patterns in transactional databases

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented.WIREs Data Mining Knowl Discov2017, 7:e1207. doi: 10.1002/widm.1207This article is categorized under:Algorithmic Development > Association RulesTechnologies > Association Rules

Mining frequent patterns in transaction databases has been a popular subject in data mining research. Common activities include finding patterns in database transactions, times-series, and exceptions. The Apriori algorithm is a widely accepted method of generating frequent patterns. The algorithm can require many scans of the database and can seriously tax resources. New methods of finding association rules, such as the Frequent Pattern Tree (FP-Tree) have improved performance, but still have problems when new data becomes available and require two scans of the database.This paper proposes a new method, which requires only one scan of the database and supports update of patterns when new data becomes available. We design a new structure called Pattern Repository (PR), which stores all of the relevant information in a highly compact form and allows direct derivation of the FP-Tree and association rules quickly with a minimum of resources. In addition, it supports run-time generation of association rules by considering only those patterns that meet on-line data requirements.

Sequential pattern is an important research topic in data mining and knowledge discovery. Sequential pattern is traditionally formed as (A, B) where A and B are frequent sequence in a transaction database. We extend this definition to include sequential patterns of forms (A, notB), (notA, B) and (notA, notB), which present negative sequential patterns among sequences. We call patterns of the form (A, B) positive sequential patterns, and patterns of the other forms negative patterns. Negative sequential patterns can also provide very useful insight view into the data set although they are different from positive ones. We put forward a discovery algorithm for mining negative sequential patterns from large transaction database in this paper.

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules....

HEPM: High-efficiency pattern mining

The mining of frequent patterns is a basic problem in data mining applications. Frequent maximal and closed itemsets mining has become an important alternative of association rule mining. In this p...

Data Mining is the field which targets the extraction and the analysis ofusable data from a large database. In this paper, we focus on the most studiedproblems in the field. That is finding closed frequent itemsets. Up to now,various graph theory techniques have been proposed to solve the frequentitemsets problem. Unfortunately, these techniques have the drawback ofneglecting the existing relationship between each pair of items that appearin the same transaction. In this paper, first of all, we present a scalable newmodeling approach which allows the representation of a transaction datasetby an undirected and labeled graph. The labels are astutely computed andproperly assigned to the bonds. Secondly, based on the clique notion ingraph theory, we propose polynomial and exact algorithm that computes allthe closed frequent itemsets. In terms of CPU-time and the memory usage,our first testing results show the efficiency of our algorithm compared torecent methods selected in the literature. Thus, the benefit of using ourgraph model is its ability to be simply extended when the correspondingdataset is updated.In other words, the proposed labeled graph incorporatesall the information contained in the transaction database. This is the strongaspect of the model, which can be used to investigate more challenging issuesrelated to the problem dealt within this paper.

Data mining identifies patterns in vast databases to support practical decision-making. Using association rule mining, the patterns in the transaction database are found and reveal client behavior. Frequent Itemset Mining (FIM) Finds Often-Buyd Items. FIM's disdain for item significance is a drawback. Practical applications depend on the relevance of the item. Thus, the High-Utility Itemset Mining (HUIM) challenge requires identifying the most profitable items in the transaction database. High-Utility Items (HUI) in the transaction database can be identified using several methods. HUIM methods based on utility lists are novel and outperform traditional methods in terms of memory consumption and execution time. The main limitation of this algorithm is the costly joins of the utility list. This research presents a highly effective swarm intelligence-based method for optimizing HUIM issues. The execution time of the suggested method is assessed. In addition, it is compared to advanced current methods. Testing on publicly available benchmark data sets shows that the swarm-based strategy outperforms current methods. This technique has wide applications in retail analytics, healthcare, fraud detection, finance, supply chain management, and recommendation systems, enabling businesses and researchers to optimize decision making and extract meaningful insights from transactional data.

Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. Such patterns reflect frequent and infrequent patterns. An abundant literature has dedicated in frequent pattern mining and tremendous efficient algorithms for frequent itemset mining in the transaction database. Nonetheless, the infrequent pattern mining has emerged to be an interesting issue in discovering patterns that rarely occur in the transaction database. More researchers reckon that rare pattern occurrences may offer valuable information in knowledge data discovery process. The R-Eclat is a novel algorithm that determines infrequent patterns in the transaction database. The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. This paper proposes IF-Postdiffset as a new variant in R-Eclat algorithm. This paper also highlights the performance of infrequent mining pattern from the transaction database among different variants of the R-Eclat algorithm regarding its execution time.

One of the recent research topics in databases is Data Mining, to find, extract and mine the useful information from databases. In case of updating transactions in the database the already discovered knowledge may become invalid. So we need efficient knowledge management techniques for finding the updated knowledge from the database. There have been lot of research in data mining, but Knowledge Management in databases is not studied much. One of the data mining techniques is to find association rules from databases. But most of association rule algorithms find association rules from transactional databases. Our research is a further step of the Tree Based Association Rule Mining (TBAR) algorithm, used in relational databases for finding the association rules .In our approach of updating the already discovered knowledge; the proposed algorithm Association Rule Update (ARU), updates the already discovered association rules found through the TBAR algorithm. Our algorithm will be able to find incremental association rules from relational databases and efficiently manage the previously found knowledge.

Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.

Data mining is aimed to extract hidden acknowledge from large dataset, in order to exploit it for predicting future trends and make decisions. Extracting meaningful and useful candidate optimally is handled by several algorithms, mainly those based on exploring incoming data, which can lose information. To address this issue, this paper proposes an algorithm named Incremental Apriori (IncA) for discovering frequent itemsets in transaction databases, which is in fact a variant of the well-known Apriori algorithm. In IncA, we introduce a notion of promising items generated from the original database, an incremental technique applied on incremental database and a health check process to ensure candidate generation completeness. On the theoretical side, our algorithm exhibits the best computational complexity compared to the recent state-of-the-art algorithms. On the other hand, we tested the proposed approach on large synthetic databases. The obtained results prove that IncA reduces the running time as well as the search space and also show that our algorithm performs better than the Apriori algorithm.

In recent years, high utility itemsets (HUIs) mining from the transactional databases becomes one of the most emerging research topic in the field of data mining due to its wide range of applications in online e-commerce data analysis, identifying interesting patterns in biomedical data and for cross marketing solutions in retail business. It aims to discover the itemsets with high utilities efficiently by considering item quantities in a transaction and profit values of each item. However, it produces a tremendous number of HUIs, which imposes further burden in analysis of the extracted patterns and also degrades the performance of mining methods. Mining the set of closed + high utility itemsets (CHUIs) solves this issue as it is a loss-less and condensed representation of all HUIs. In this paper, we aim to present a new algorithm for finding CHUIs from a transactional database, called the CHUM (Closed + High Utility itemset Miner), which is scalable and efficient. The proposed mining algorithm adopts a tricky aimed vertical representation of the database in order to speed up the execution time in generating itemset closures and compute their utility information without accessing the database. The proposed method makes use of the item co-occurrences strategy in order to further reduce the number of intersections needed to be performed. Several experiments are conducted on various sparse and dense datasets and the simulation results clearly show the scalability and superior performance of our algorithm as compared to those for the existing state-of-the-art CHUD (Closed + High Utility itemset Discovery) algorithm.

Data mining aims to develop a new generation of tools to intelligently assist humans in analyzing mountains of data. Over the past few years, great progress has been made in both research and applications of data mining. Data mining systems have helped many businesses by exposing previously unknown patterns in their databases, which were used to improve profits, enhance customer services, and ultimately achieve a competitive advantage. In this paper, we present our unique data mining system DM-II (Data Mining Integration and Interestingness). DM-II is a PC-based system working in Windows 95/98/NT environment. Apart from the normal components of a data mining system, DM-II has a number of unique and advanced sub-systems. These sub-systems have been applied in many real-life applications, including education applications, insurance application, accident application, disease application, drug screening application, image classification, etc. Here, we focus on discussing the following sub-systems: CBA: CBA originally stands for Classification-Based on Associations [lo]. It has now been extended with a number of other advanced features. In all, CBA has the following capabilities: Building classifiers using association rules: Traditionally, association rule mining and classifier building are regarded as two different data mining tasks. CBA unifies the two tasks. It is able to ,generate association rules and builds a classifier using a special subset of the association rules. What is significant is that CBA, in general, produces more accurate classifiers compared to the state-ofthe-art classification system C4.5 [ 161. It also helps to solve some outstanding problems with the existing classification systems. Pruning and summarizing the discovered associations: One major problem with association

This chapter introduces the main data-mining approaches and the works of researchers that applied them in the smart home context. It provides understandable material to exploit data mining for activity recognition in a smart home. The chapter discusses the subject to be used as a basis for further development and research. It discusses the general knowledge related to data mining. The chapter defines what it is and describes the main challenges related to research and application of data mining. The principles are reviewed along with the main limitations for their application in smart home research. The chapter covers the association rules on mining algorithms. It reviews the basic principles and the main works related to smart home research. The chapter investigates clustering for activity recognition. It explains the main algorithms and provides examples of their applications.