Efficient mining of maximal correlated weight frequent patterns

Yun, Unil; Ryu, Keun Ho

doi:10.3233/ida-130612

Cited by 28 publications

(5 citation statements)

References 40 publications

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“…These approaches consider the effective utilization of weight conditions to traditional frequent pattern mining as well as other pattern mining techniques. For instance, there are many algorithms such as maximal weighted frequent pattern mining [20,23], weight-based periodic pattern mining [19], and data stream mining based on weight constraints [25]. In addition, various weightbased approaches have been studied [7,8].…”

Section: Weighted Frequency(x)mentioning

confidence: 99%

Mining weighted erasable patterns by using underestimated constraint-based pruning technique

Lee

Yun

Ryang

2015

Journal of Intelligent &Amp; Fuzzy Systems

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Erasable pattern mining is one of the variations in frequent pattern mining, and its main goal is to maximize the production capacity of manufacturing industries by quickly overcoming the financial crises that can occur in industries. However, existing erasable pattern mining approaches utilize only profit information for each product, but do not consider distinct weights of items organizing the products. In addition, previous algorithms spend much time and space mining erasable patterns due to their inefficient pattern mining methods. In a real-life environment, considering weights of items in each product can be more important compared to calculating product profits only. For this reason, we propose a novel algorithm for mining weighted erasable patterns by considering the distinct weight of each item. Moreover, we discuss both discovering weighted erasable patterns and minimizing the resource availability of erasable pattern mining processes utilizing weight conditions. Especially, our approach has advantages for time and space resource consumption compared to existing approaches because our algorithm uses a pattern pruning method and stores item information included in product databases by using both compact tree and hash list structures. We present performance evaluation utilizing both real and synthetic datasets to demonstrate the efficiency of our algorithm.

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Section: Weighted Frequency(x)mentioning

confidence: 99%

Mining weighted erasable patterns by using underestimated constraint-based pruning technique

Lee

Yun

Ryang

2015

Journal of Intelligent &Amp; Fuzzy Systems

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“…Frequent pattern mining has been one of the most popular research topics in the data mining area due to its broad applications in mining association rules, 2,6,29,33 correlations, 39,42 sequential patterns, 5,11,15,12,45 compressed sets, 34 approximate patterns, 1,7,8,19,21,40,46 steam data, 9,18,20,23,43 graph patterns, 22,36 high utility patterns, 27,28,44 top-k patterns, 26 and many other data mining tasks. These approaches have focused on three aspects.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints

Yun

Yoon

2014

Int. J. Unc. Fuzz. Knowl. Based Syst.

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Based on the frequent pattern mining, closed frequent pattern mining and weighted frequent pattern mining have been studied to reduce the search space and discover important patterns. In the previous definition of weighted closed patterns, supports of patterns are only considered to compute the closures of the patterns. It means that the closures of weighted frequent patterns cannot be perfectly checked. Moreover, the usefulness of weighted closed frequent patterns depends on the presence of frequent patterns that have supersets with the exactly same weighted support. However, from the errors such as noise, slight changes in items' supports or weights by them have significantly negative effects on the mining results, which may prevent us from obtaining exact and valid analysis results since the errors can break the original characteristics of items and patterns. In this paper, to solve the above problems, we propose a concept of robust weighted closed frequent pattern mining, and an approximate bound is defined on the basis of the concept, which can relax requirements for precise equality among patterns' weighted supports. Thereafter, we propose a weighted approximate closed frequent pattern mining algorithm which not only considers the two approaches but also suggests fault tolerant pattern mining in the noise constraints. To efficiently mine weighted approximate closed frequent patterns, we suggest pruning and subset checking methods which reduce search space. We also report extensive performance study to demonstrate the effectiveness, efficiency, memory usage, scalability, and quality of patterns in our algorithm.

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“…Some variants of FIs such as high utility itemsets (itemsets whose utility satisfies a given threshold), top‐ k high utility itemsets (top‐k itemsets with highest utility), weighted pattern (pattern with weighted items), erasable itemsets (itemsets can be eliminated but do not greatly affect the factory's profit), weighted erasable patterns (erasable itemsets considered the distinct weight of each item), and so on are proposed. Besides, several type of representations that limit the number of FIs such as FCIs, FMIs, top‐ k FIs, top‐rank‐ k FIs, and FIs with constraints are also proposed. In traditional approaches for mining ARs, researchers usually focus on the first phrase (mining FIs/FCIs/FMIs).…”

Section: Introductionmentioning

confidence: 99%

The lattice‐based approaches for mining association rules: a review

2016

WIREs Data Min & Knowl

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The traditional methods for mining association rules (ARs) include two phrases: mining frequent itemsets (FIs)/frequent closed itemsets (FCIs)/frequent maximal itemsets (FMIs) and generating ARs from FIs/FCIs/FMIs. Lattice‐based approaches (LBAs) for mining ARs are new approaches including two phrases: frequent itemset lattice (FIL)/frequent closed itemset lattice (FCIL) building and generating ARs from the lattice. Total mining time of LBAs for mining ARs outperforms the traditional methods for mining ARs. Besides, the most important advantage of LBAs for mining ARs is that the algorithms only build the lattice once and mine ARs with many different confidences or many different minimum supports (the thresholds have to be greater than or equal to the threshold used to build lattices) without mining FIs/FCIs again. In this article, we describe a number of existing LBAs for mining ARs on static databases including lattice building and rule generation. In addition, in today's online system, the data often change in several operations such as insert, delete, and update. Hence, a number of LBAs for mining ARs on dynamic databases are mentioned. Finally, complexity analysis of the LBAs for mining ARs is also thoroughly discussed. WIREs Data Mining Knowl Discov 2016, 6:140–151. doi: 10.1002/widm.1181 This article is categorized under: Algorithmic Development > Association Rules

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Efficient mining of maximal correlated weight frequent patterns

Cited by 28 publications

References 40 publications

Mining weighted erasable patterns by using underestimated constraint-based pruning technique

Mining weighted erasable patterns by using underestimated constraint-based pruning technique

An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints

The lattice‐based approaches for mining association rules: a review

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