Multi-criteria Utility Mining Using Maximum Constraints

Computational Collective Intelligence. Technologies and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-11289-3_47 ◽

2014 ◽

pp. 466-471 ◽

Cited By ~ 1

Author(s):

Guo-Cheng Lan ◽

Tzung-Pei Hong ◽

Yu-Te Chao

Keyword(s):

Utility Mining

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Analysis of Different Utility Mining Methodologies in Transactional Databases

International Journal of Big Data Security Intelligence ◽

10.21742/ijbdsi.2016.3.1.01 ◽

2016 ◽

Vol 3 (1) ◽

pp. 1-10

Author(s):

Divvela SrinivasaRao ◽

◽

V. Sucharita ◽

Keyword(s):

Utility Mining ◽

Transactional Databases

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On-Shelf Utility Mining of Sequence Data

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3457570 ◽

2021 ◽

Vol 16 (2) ◽

pp. 1-31

Author(s):

Chunkai Zhang ◽

Zilin Du ◽

Yuting Yang ◽

Wensheng Gan ◽

Philip S. Yu

Keyword(s):

High Efficiency ◽

Sequence Data ◽

Real Life ◽

Search Space ◽

Upper Bounds ◽

Utility Mining ◽

Limited Memory ◽

Time Periods ◽

High Utility ◽

Synthetic Datasets

Utility mining has emerged as an important and interesting topic owing to its wide application and considerable popularity. However, conventional utility mining methods have a bias toward items that have longer on-shelf time as they have a greater chance to generate a high utility. To eliminate the bias, the problem of on-shelf utility mining (OSUM) is introduced. In this article, we focus on the task of OSUM of sequence data, where the sequential database is divided into several partitions according to time periods and items are associated with utilities and several on-shelf time periods. To address the problem, we propose two methods, OSUM of sequence data (OSUMS) and OSUMS + , to extract on-shelf high-utility sequential patterns. For further efficiency, we also design several strategies to reduce the search space and avoid redundant calculation with two upper bounds time prefix extension utility ( TPEU ) and time reduced sequence utility ( TRSU ). In addition, two novel data structures are developed for facilitating the calculation of upper bounds and utilities. Substantial experimental results on certain real and synthetic datasets show that the two methods outperform the state-of-the-art algorithm. In conclusion, OSUMS may consume a large amount of memory and is unsuitable for cases with limited memory, while OSUMS + has wider real-life applications owing to its high efficiency.

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