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Your search keyword '"Gan, Wensheng"' showing total 2 results
Start Over Author "Gan, Wensheng" Topic projection Topic utility theory
2 results on '"Gan, Wensheng"'

1. ProUM: Projection-based utility mining on sequence data.

Author

Gan, Wensheng, Lin, Jerry Chun-Wei, Zhang, Jiexiong, Chao, Han-Chieh, Fujita, Hamido, and Yu, Philip S.

Subjects
*SEQUENTIAL pattern mining, *DATA mining, *UTILITY theory, *ACCOUNTING
Abstract

Utility is an important concept in Economics. A variety of applications consider utility in real-life situations, which has lead to the emergence of utility-oriented mining (also called utility mining) in the recent decade. Utility mining has attracted a great amount of attention, but most of the existing studies have been developed to deal with itemset-based data. Time-ordered sequence data is more commonly seen in real-world situations, which is different from itemset-based data. Since they are time-consuming and require large amount of memory usage, current utility mining algorithms still have limitations when dealing with sequence data. In addition, the mining efficiency of utility mining on sequence data still needs to be improved, especially for long sequences or when there is a low minimum utility threshold. In this paper, we propose an efficient Pro jection-based U tility M ining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store the necessary information of the sequence-order and utility. ProUM can significantly improve the mining efficiency by utilizing the projection technique in generating utility-array, and it effectively reduces the memory consumption. Furthermore, a new upper bound named sequence extension utility is proposed and several pruning strategies are further applied to improve the efficiency of ProUM. By taking utility theory into account, the derived high-utility sequential patterns have more insightful and interesting information than other kinds of patterns. Experimental results showed that the proposed ProUM algorithm significantly outperformed the state-of-the-art algorithms in terms of execution time, memory usage, and scalability. [ABSTRACT FROM AUTHOR]

Published
2020
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2. Extracting non-redundant correlated purchase behaviors by utility measure.

Author

Gan, Wensheng, Lin, Jerry Chun-Wei, Fournier-Viger, Philippe, Chao, Han-Chieh, and Fujita, Hamido

Subjects
*FEATURE extraction, *UTILITY theory, *MEASURE theory, *CONSUMER behavior, *DATA mining, *COMPUTER users
Abstract

From web search and data mining, users’ click and purchase behaviors contain valuable information, thus numerous approaches have been proposed to identify embedded useful knowledge from them. In these real-life situations, each user may perform the same action/event multiple times, and multiple accessed events product different profit. Many utility-oriented data mining approaches thus have been extensively studied. Previous studies have the limitation that the overall utility of traditional pattern is limited since they rarely consider the inherent correlation. For example, from the purchase behavior, the low-utility patterns sometimes with a very high-utility pattern will be considered as a valuable pattern even if this behavior may be not highly correlated. A more intelligent framework that provides non-redundant and correlated behavior based on utility measure is thus desired. In this paper, we first present a novel method to extract non-redundant correlated purchase behaviors considering the utility and correlation factors. The high qualified patterns can be derived with high profit and strong correlation, which can lead to higher recall and reveal better precision. In the proposed projection-based approach, an efficient projection mechanism and a sorted downward closure property are developed to reduce the database size. Several pruning strategies are further developed to efficiently and effectively discover the desired patterns. An extensive experimental study showed that the novel non-redundant correlated high-utility pattern has more effectiveness than the previous knowledge representation. Moreover, the proposed algorithm is efficient in terms of execution time and memory usage. [ABSTRACT FROM AUTHOR]

Published
2018
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