An effective tree structure for mining high utility itemsets

Lin, Chun–Wei; Hong, Tzung-Pei; Lu, Wen-Hsiang

doi:10.1016/j.eswa.2010.12.082

Cited by 226 publications

(89 citation statements)

References 13 publications

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“…Afterwards, Tseng et al [9] proposed an efficient approach called UP-Growth, in which several pruning strategies have been applied to reduce the number of candidate patterns in phase I of the Two-Phase algorithm. Lin et al [10] proposed an efficient tree structure called HUP-Tree, and applied the HUPGrowth mining algorithm. The HUP-Growth algorithm achieved better performance over the TwoPhase algorithm.…”

Section: High Utility Pattern Miningmentioning

confidence: 99%

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Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams

Manike¹,

Om²

2015

IJCIS

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High utility pattern mining is an emerging research topic in the data mining field. Unlike frequent pattern mining, high utility pattern mining deals with non-binary databases, in which the information about purchased quantities of items is maintained. Due to the non-existence of anti-monotone property among the utilities of itemsets, utility mining becomes a big challenge. Moreover, discovering useful patterns from the huge number of potential patterns is a mining bottleneck. However, the compact (Closed and Maximal) high utility pattern mining moderately lessens the number of patterns, but it does not solve it. Recently, an efficient framework called GUIDE, was proposed in the literature to address this issue. Though, GUIDE effectively reduced the number of high utility patterns, yet the quality of few mined patterns and their utilities are not exact. In view of this, we propose a modified MGUIDE LM algorithm to improve the quality and determine exact utilities of maximal patterns.

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Section: High Utility Pattern Miningmentioning

confidence: 99%

“…After generating patterns/projections from a transaction, MMUI-Tree is constructed by inserting all these patterns. In a header table [10], the items are maintained in alphabetical order, each item linked to its node in the MMUI-Tree. Thus, the process of updating patterns utilities becomes more efficient.…”

Section: -Itemsets {{Imentioning

confidence: 99%

Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams

Manike¹,

Om²

2015

IJCIS

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“…A TWU of an itemset is an upper bound of its utility, which keeps the downward closure property [2]. Based on TWU, Lin [8] proposed the high utility pattern tree (HUP-tree) algorithm for mining high utility itemsets without candidate generation. In [9], a pattern growth approach named UP-growth was proposed for mining high utility itemsets within two scans of a database.…”

Section: High Utility Itemset Mining (Huim)mentioning

confidence: 99%

Mining High Utility Itemsets with Regular Occurrence

et al. 2016

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Abstract. High utility itemset mining (HUIM) plays an important role in the data mining community and in a wide range of applications. For example, in retail business it is used for finding sets of sold products that give high profit, low cost, etc. These itemsets can help improve marketing strategies, make promotions/ advertisements, etc. However, since HUIM only considers utility values of items/itemsets, it may not be sufficient to observe product-buying behavior of customers such as information related to "regular purchases of sets of products having a high profit margin". To address this issue, the occurrence behavior of itemsets (in the term of regularity) simultaneously with their utility values was investigated. Then, the problem of mining high utility itemsets with regular occurrence (MHUIR) to find sets of co-occurrence items with high utility values and regular occurrence in a database was considered. An efficient single-pass algorithm, called MHUIRA, was introduced. A new modified utility-list structure, called NUL, was designed to efficiently maintain utility values and occurrence information and to increase the efficiency of computing the utility of itemsets. Experimental studies on real and synthetic datasets and complexity analyses are provided to show the efficiency of MHUIRA combined with NUL in terms of time and space usage for mining interesting itemsets based on regularity and utility constraints.

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“…IHUP [11] was then proposed by Ahmed et al to efficiently mine high utility itemsets and it uses a tree-like data structure. Some other widely studied high utility itemsets mining algorithms are HUP-tree [6] by Lin et al UP-growth and UP-growth+ [7] by Tseng et al MHU-Growth [22] for mining high utility itemsets with multiple minimum support was first proposed by Ryanga et al HUIM-MMU [21] for Mining high utility itemsets with multiple minimum utility thresholds was then proposed by Lin et al Our study aims to remove the fundamental research gap between MHU-Growth and HUIM-MMU and use multiple minimum support and multiple minimum utility thresholds to efficiently discover all high utility itemsets.…”

Section: Related Workmentioning

confidence: 99%