An efficient algorithm for mining high utility itemsets with negative item values in large databases

Chu, Chun-Jung; Tseng, Vincent S.; Liang, Tyne

doi:10.1016/j.amc.2009.05.066

Cited by 66 publications

(59 citation statements)

References 11 publications

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“…This issue has been investigated by Chu, Tseng, and Liang (2009) who propose the HUINIV-Mine algorithm. HUINIV-Mine uses the TP algorithm to mine HUI.…”

Section: Search Spacementioning

confidence: 98%

“…Association rule mining (ARM) algorithms consider only the presence or absence of items in a complete transaction; they do not reflect semantic factors such as cost, profit, and so forth. High utility pattern mining algorithms (Ahmed et al 2009;Ahmed, Tanbeer, and Jeong 2010a;Chu, Tseng, and Liang 2009;Erwin, Gopalan, and Achuthan 2007a;Li 2011;Liu, Liao, and Choudhary 2005a;Tseng, Chu, and Liang 2006;Tseng et al 2010;Wu et al 2012;Yao and Hamilton 2006;Yin, Zheng, and Cao 2012) resolve the problems in ARM by considering nonbinary values in transactions and different profit values of every item.…”

Section: Introductionmentioning

confidence: 97%

“…To address this issue, Chu, Tseng, and Liang (2009) developed a scheme named high utility itemsets with negative item values (HUINIV-Mine) for mining HUI with negative item values from large databases. The HUINIV-Mine scheme makes use of a two-phase (TP; Liu, Liao, and Choudhary 2005b) algorithm to mine HUI from the databases.…”

Section: Introductionmentioning

confidence: 99%

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Discovery of High Utility Itemsets Using Genetic Algorithm with Ranked Mutation

Kannimuthu¹,

Premalatha

2014

Applied Artificial Intelligence

102

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Utility mining is the study of itemset mining from the consideration of utilities. It is the utility-based itemset mining approach to find itemsets conforming to user preferences. Modern research in mining high-utility itemsets (HUI) from the databases faces two major challenges: exponential search space and database-dependent minimum utility threshold. The search space is extremely vast when the number of distinct items and the size of the database are very large. Data analysts must specify suitable minimum utility thresholds for their mining tasks, although they might have no knowledge pertaining to their databases. Moreover, a utility-mining algorithm supports only an itemset with positive item values. To evade these problems, two approaches are presented for mining HUI containing negative item values from transaction databases: with/without specifying the minimum utility threshold through a genetic algorithm with ranked mutation. To the best of our knowledge, this is the first work on mining HUI with negative item values from transaction databases using a genetic algorithm. Experimental results show that approaches described in this article achieve better performance in terms of scalability and efficiency.

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“…This issue has been investigated by Chu, Tseng, and Liang (2009) who propose the HUINIV-Mine algorithm. HUINIV-Mine uses the TP algorithm to mine HUI.…”

Section: Search Spacementioning

confidence: 98%

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Discovery of High Utility Itemsets Using Genetic Algorithm with Ranked Mutation

Kannimuthu¹,

Premalatha

2014

Applied Artificial Intelligence

102

View full text Add to dashboard Cite

show abstract

“…To address these issues, utility mining [2,5,6,7,8,11,13,15,19,20,24,26] emerges as an important topic in data mining. In utility mining, each item has a weight (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…An itemset is called a high utility itemset (abbreviated as HUI) if its utility is no less than a userspecified minimum utility threshold; otherwise, it is called a low utility itemset. Utility mining is an important task and has a wide range of applications such as website click stream analysis [2,5,19,24], cross-marketing in retail stores [6,20,26] and biomedical applications [7].…”

Section: Introductionmentioning

confidence: 99%

Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets

Fournier-Viger

et al. 2011

2011 IEEE 11th International Conference on Data Mining

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Abstract-Mining high utility itemsets from transactional databases is an important data mining task, which refers to the discovery of itemsets with high utilities (e.g. high profits). Although several studies have been carried out, current methods may present too many high utility itemsets for users, which degrades the performance of the mining task in terms of execution time and memory requirement. To achieve high efficiency for the mining task and provide a concise mining result to users, we propose a novel framework in this paper for mining closed + high utility itemsets, which serves as a compact and lossless representation of high utility itemsets. We present an efficient algorithm called CHUD (Closed + High utility itemset Discovery) for mining closed + high utility itemsets. Further, a method called DAHU (Derive All High Utility itemsets) is proposed to recover all high utility itemsets from the set of closed + high utility itemsets without accessing the original database. Results of experiments on real and synthetic datasets show that CHUD and DAHU are very efficient and that our approach achieves a massive reduction in the number of high utility itemsets (up to 800 times in our experiments). In addition, when all high utility itemsets can be recovered by DAHU, the combination of CHUD and DAHU outperforms the state-of-the-art algorithms for mining high utility itemsets.

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Mining high utility pattern with negative items in dynamic databases

Han

Zhang

Wang

et al. 2021

Int J of Intelligent Sys

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High utility pattern mining with negative items (HUPMN) has more practical applications because it can process data with negative utility values. But the existing HUPMN algorithms assume that the database is static and it would be very expensive to use these algorithms directly to deal with dynamic databases. To cope with this challenge, an high utility pattern algorithm for mining negative items from an incremental database is proposed for the first time, and an incremental index list structure is designed, which uses index values to quickly access and update the information stored in the list. In addition, A memory reuse strategy is also applied to reduce memory usage. Finally, a HUPMN algorithm based on sliding window was proposed, which can quickly update item information when the window was sliding. Extensive experimental evaluations have been carried out on a variety of data sets, and the results show that the proposed algorithm exhibits excellent performance in terms of running time and memory usage.

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An efficient algorithm for mining high utility itemsets with negative item values in large databases

Cited by 66 publications

References 11 publications

Discovery of High Utility Itemsets Using Genetic Algorithm with Ranked Mutation

Discovery of High Utility Itemsets Using Genetic Algorithm with Ranked Mutation

Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets

Mining high utility pattern with negative items in dynamic databases

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