An efficient method for mining high utility closed itemsets

Nguyen, Loan T. T.; Vu, Vinh V.; Lam, Mi T.H.; Duong, Thuy T.M.; Manh, Ly T.; Nguyễn, Thùy; Vo, Bay; Fujita, Hamido

doi:10.1016/j.ins.2019.05.006

Cited by 60 publications

(19 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…dHAUIM [21] takes into account the length of the pattern and finds high average utility patterns. In traditional utility mining, because the resulting HUIs set is too large and requires a lot of computational resources, HMiner-Closed [22], a high utility closed pattern mining technique for finding small sets representing HUIs, was proposed. HUOPM [23] considers frequency, utility, and occupancy and discovers high utility occupancy patterns.…”

Section: Related Work a High Utility Pattern Mining From Static mentioning

confidence: 99%

Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

Nam

Yun

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Traditional pattern mining is designed to handle binary database that assume all items in the database have same importance, there is a limitation to recognize accurate information from real-world databases using traditional method. To solve this problem, the high utility pattern mining approaches from non-binary database have been proposed and actively studied by many researchers. Lately, new data is progressively created with the passage of time in diverse area such as biometric data of a patient diagnosed in a medical device and log data of an internet user, and the volume of a database is gradually increasing. A database with these characteristics is called a dynamic database. Under these circumstances, high utility mining techniques suitable for analyzing dynamic databases have recently been extensively studied. In this paper, we propose a new list-based algorithm that mines high utility patterns considering the arrival time of each transaction in an incremental database environment. That is, our algorithm efficiently performs pattern pruning by using a damped window model that considers the importance of the previously inputted data lower than that of recently inserted data and identifies high utility patterns. Experimental results indicate that our proposed method has better performance than the state-of-the-art techniques in terms of runtime, memory, and scalability. INDEX TERMS Data mining, damped window model, pattern pruning, high utility patterns, stream data mining.

show abstract

Section: Related Work a High Utility Pattern Mining From Static mentioning

confidence: 99%

Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

Nam

Yun

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is an indication for the items that satisfies the minimum support value in the transactional database. The value of the item support is calucated by (1) Confidence: Once the highly occurence patterns are discovered, then generalized association rules which satisifies the minimum confidence are created. It denotes how frequently the rules have been established to be true.…”

Section: Pruningmentioning

confidence: 99%

“…Discovering patterns from huge databases and consuming the related information in different aspects is referred as data mining. It helps in various disciplines as given in Fig.1 for searching appropriate solutions of their problems [1].…”

Section: Introductionmentioning

confidence: 99%

An improved Frequent Pattern Mining in Sustainable Learning Practice using Generalized Association Rules

2019

IJITEE

View full text Add to dashboard Cite

This research focuses on mining the frequent patterns occurred in the given Datasets by Generalization of Association Rules. Frequent pattern mining is a significant as well as interesting problem in the research filed of Data Mining. Building of frequent pattern tree (FP tree), frequent pattern growth (FP growth) and association rule generation are implemented to find the repeated patterns of data. FP tree Construction Algorithm is mainly used to compact a vast database into a extremely compressed, seems to very tiny data structure; hence eliminates the repeated scanning of database. The role of FP growth algorithm is to discover the frequent patterns with FP tree structure and construct the generalized association rules corresponding to the learning data. FP growth algorithm obtained best results as compared with conventional Apriori algorithm. This research provides some practical real time applications pertaining data mining techniques in the field of learning, education, marketing, finance and so on.

show abstract

“…It focuses on extracting subsequences with a high utility (importance) from quantitative sequential databases. Current HUSP mining algorithms, however, only consider occurring events and do not take non-occurring events into account, which results in the loss of a lot of useful information [9][10][11]. Thus, high utility negative sequential pattern (HUNSP) mining is proposed to address this issue by considering both occurring events and non-occurring events.…”

Section: Introductionmentioning

confidence: 99%

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

Zhang

et al. 2020

Symmetry

View full text Add to dashboard Cite

As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent’s local utility value in a HUNSR, a key issue in calculating the utility-confidence of the HUNSR. To address the intrinsic complexities, we propose a comprehensive algorithm called e-HUNSR and the contributions are as follows. (1) We formalize the problem of HUNSR mining by proposing a series of concepts. (2) We propose a novel data structure to store the related information of HUNSR candidate (HUNSRC) and a method to efficiently calculate the local utility value and utility of HUNSRC’s antecedent. (3) We propose an efficient method to generate HUNSRC based on high utility negative sequential pattern (HUNSP) and a pruning strategy to prune meaningless HUNSRC. To the best of our knowledge, e-HUNSR is the first algorithm to efficiently mine HUNSR. The experimental results on two real-life and 12 synthetic datasets show that e-HUNSR is very efficient.

show abstract

An efficient method for mining high utility closed itemsets

Cited by 60 publications

References 28 publications

Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

An improved Frequent Pattern Mining in Sustainable Learning Practice using Generalized Association Rules

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

Contact Info

Product

Resources

About