An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports

Hu, Ya-Han; Wu, Fan; Liao, Yi-Jiun

doi:10.1016/j.jss.2012.12.020

Cited by 30 publications

(14 citation statements)

References 16 publications

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“…To address this issue, multiple minimum supports model has been studied [6][7][8]11,12,17,21]. In this model, each item has a different support threshold value according to their frequency.…”

Section: Related Workmentioning

confidence: 99%

Discovering high utility itemsets with multiple minimum supports

Ryang

Yun

Ryu

2014

IDA

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Generally, association rule mining uses only a single minimum support threshold for the whole database. This model implicitly assumes that all items in the database have the same nature. In real applications, however, each item can have different nature such as medical datasets which contain information of both diseases and symptoms or status related to the diseases. Therefore, association rule mining needs to consider multiple minimum supports. Association rule mining with multiple minimum supports discovers all item rules by reflecting their characteristics. Although this model can identify meaningful association rules including rare item rules, not only the importance of items such as fatality rate of diseases but also attribute of items such as duration of symptoms are not considered since it treats each item with equal importance and represents the occurrences of items in transactions as binary values. In this paper, we propose a novel tree structure, called MHU-Tree (Multiple item supports with High Utility Tree), which is constructed with a single scan. Moreover, we propose an algorithm, named MHU-Growth (Multiple item supports with High Utility Growth), for mining high utility itemsets with multiple minimum supports. Experimental results show that MHU-Growth outperforms the previous algorithm on both real and synthetic datasets, and can discover useful rules from a medical dataset.

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Section: Related Workmentioning

confidence: 99%

Discovering high utility itemsets with multiple minimum supports

Ryang

Yun

Ryu

2014

IDA

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“…In essence, Apriori-based algorithms use breadth-first search and Apriori pruning, which generate large sets of candidates for growing longer sequences [15]. To reduce the large sets of candidates, pattern-growth algorithms have been developed with the core idea of divide and conquer [16][17][18][19]. As the complete set of sequential patterns can be partitioned into different subsets according to different prefixes, the number of candidates decreases substantially but at the cost of increasing the corresponding projected databases.…”

Section: Introductionmentioning

confidence: 99%

Normalized-Mutual-Information-Based Mining Method for Cascading Patterns

Xue

Liu

et al. 2016

IJGI

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Abstract:A cascading pattern is a sequential pattern characterized by an item following another item in order. Recent research has investigated a challenge of dealing with cascading patterns, namely, the exponential time dependence of database scanning with respect to the number of items involved. We propose a normalized-mutual-information-based mining method for cascading patterns (M 3 Cap) to address this challenge. M 3 Cap embeds mutual information to reduce database-scanning time. First, M 3 Cap calculates the asymmetrical mutual information between items with one database scan and extracts pair-wise related items according to a user-specified information threshold. Second, a one-level cascading pattern is generated by scanning the database once for each pair-wise related item at the quantitative level. Third, a recursive linking-pruning-generating loop generates an (m + 1)-level-candidate cascading pattern from m-dimensional patterns on the basis of antimonotonicity and non-additivity, repeating this step until no further candidate cascading patterns are generated. Fourth, meaningful cascading patterns are generated according to user-specified minimum evaluation indicators. Finally, experiments with remote sensing image datasets covering the Pacific Ocean demonstrate that the computation time of recursive linking and pruning is significantly less than that of database scanning; thus, M 3 Cap improves performance by reducing database scanning while increasing intensive computing.

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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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An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports

Cited by 30 publications

References 16 publications

Discovering high utility itemsets with multiple minimum supports

Discovering high utility itemsets with multiple minimum supports

Normalized-Mutual-Information-Based Mining Method for Cascading Patterns

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

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