• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/

Performance Analysis of Siding Window based Stream High Utility Pattern Mining Methods


Heungmo Ryang, Unil Yun, Journal of Internet Computing and Services, Vol. 17, No. 6, pp. 53-60, Dec. 2016
10.7472/jksii.2016.17.6.53, Full Text:
Keywords: Pattern mining, High utility pattern mining, sliding window model, resource-limited environments

Abstract

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.


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Cite this article
[APA Style]
Ryang, H. & Yun, U. (2016). Performance Analysis of Siding Window based Stream High Utility Pattern Mining Methods. Journal of Internet Computing and Services, 17(6), 53-60. DOI: 10.7472/jksii.2016.17.6.53.

[IEEE Style]
H. Ryang and U. Yun, "Performance Analysis of Siding Window based Stream High Utility Pattern Mining Methods," Journal of Internet Computing and Services, vol. 17, no. 6, pp. 53-60, 2016. DOI: 10.7472/jksii.2016.17.6.53.

[ACM Style]
Heungmo Ryang and Unil Yun. 2016. Performance Analysis of Siding Window based Stream High Utility Pattern Mining Methods. Journal of Internet Computing and Services, 17, 6, (2016), 53-60. DOI: 10.7472/jksii.2016.17.6.53.