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Barber, Brock; Hamilton, Howard J.

doi:10.1023/a:1022419032620

Cited by 65 publications

(1 citation statement)

References 26 publications

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“…To reduce the number of candidates in the Two-Phase algorithm, Li et al (2008) proposed an isolated item discarding strategy (abbreviated as IIDS). The IIDS shows that an itemset share mining (Barber & Hamilton, 2003) problem can be converted to a utility mining problem by replacing the frequency value of each item in a transaction by its total profit. By pruning isolated items during the level-wise search, the number of HTWUIs can be further reduced.…”

Section: Existing Algorithmsmentioning

confidence: 99%

Bahui

Song

2014

International Journal of Data Warehousing and Mining

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Mining high utility itemsets is one of the most important research issues in data mining owing to its ability to consider nonbinary frequency values of items in transactions and different profit values for each item. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. In this paper, the authors propose an efficient algorithm, namely BAHUI (Bitmap-based Algorithm for High Utility Itemsets), for mining high utility itemsets with bitmap database representation. In BAHUI, bitmap is used vertically and horizontally. On the one hand, BAHUI exploits a divide-and-conquer approach to visit itemset lattice by using bitmap vertically. On the other hand, BAHUI horizontally uses bitmap to calculate the real utilities of candidates. Using bitmap compression scheme, BAHUI reduces the memory usage and makes use of the efficient bitwise operation. Furthermore, BAHUI only records candidate high utility itemsets with maximal length, and inherits the pruning and searching strategies from maximal itemset mining problem. Extensive experimental results show that the BAHUI algorithm is both efficient and scalable.

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Section: Existing Algorithmsmentioning

confidence: 99%

Bahui

Song

2014

International Journal of Data Warehousing and Mining

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A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets

Liu

Liao

Choudhary

2005

Lecture Notes in Computer Science

566

523

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A Measurement-Theoretic Foundation of Rule Interestingness Evaluation

Yao

Chen

Yang

Foundations and Novel Approaches in Data Mining

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Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant. CanadaReproduced with permission of the copyright owner. Further reproduction prohibited without permission. UNIVERSITY OF REGINA FACULTY OF GRADUATE STUDIES AND RESEARCH SUPERVISORY AND EXAMINING COMMITTEE Yaohua Chen, candidate for the degree of Master of Science, has presented a thesis titled, A Measurement-Theoretic Foundation of Rule Interestingness Evaluation, in an oral examination held on August 30, 2004. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material.

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Untitled

Cited by 65 publications

References 26 publications

Bahui

Bahui

A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets

A Measurement-Theoretic Foundation of Rule Interestingness Evaluation

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