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http://dx.doi.org/10.3745/KIPSTD.2006.13D.6.765

Mining Association Rules in Multidimensional Stream Data  

Kim, Dae-In (전남대학교 전자컴퓨터정보통신공학부)
Park, Joon (전남대학교 전산학과)
Kim, Hong-Ki (동신대학교 컴퓨터학과)
Hwang, Bu-Hyun (전남대학교 전자컴퓨터정보통신공학부)
Publication Information
The KIPS Transactions:PartD / v.13D, no.6, 2006 , pp. 765-774 More about this Journal
Abstract
An association rule discovery, a technique to analyze the stored data in databases to discover potential information, has been a popular topic in stream data system. Most of the previous researches are concerned to single stream data. However, this approach may ignore in mining to multidimensional stream data. In this paper, we study the techniques discovering the association rules to multidimensional stream data. And we propose a AR-MS method reflecting the characteristics of stream data since make the summarization information by one data scan and discovering the association rules for significant rare data that appear infrequently in the database but are highly associated with specific event. Also, AR-MS method can discover the maximal frequent item of multidimensional stream data by using the summarization information. Through analysis and experiments, we show that AR-MS method is superior to other previous methods.
Keywords
StreAm Data; Stream Data Mining; Association Rule; Significant Rare Itemsets; Maximal Frequent Itemsets;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy, 'Mining Data Streams: A Review,' SIGMOD Record, Vol.34, No.2, pp.18-26, June, 2005   DOI   ScienceOn
2 B. Brian, S. Babu, M. Datar, R. Motwani, and J. Widom, 'Models and Issues in Data Stream Systems,' In Proc. of PODS, March, 2002   DOI
3 M. J. Franklin, S. R. Jeffery, S. Krishnamurthy, F. Reiss, S. Rizvi, E. Wu, O. Cooper, A. Edakkunni, and W. Hong, 'Design Consideration for High Fan-in Systems: The HiFi Approach,' In Proc. of CIDR, pp.290-304, Jan., 2005
4 H. Li, S. Lee, and M. Shan, 'Online Mining (Recently) Maximal Frequent Itemsets over Data Streams,' In Proc. of RIDE-SDMA '05, pp.11-18, April, 2005   DOI
5 A. Deligiannakis, Y. Kotidis, and N. Roussopoulos, 'Hierarchical In-Network Data Aggregation with Quality Guarantees,' LNCS(EDBT 2004), pp.658-675, March, 2004
6 하단심, 황부현, '상대 지지도를 이용한 의미 있는 희소 항목에 대한 연관 규칙 탐사 기법' 정보과학회 논문지 데이터베이스 제 28권 제 4호, pp.577-586, 2001   과학기술학회마을
7 장중혁, 이원석, '데이터 스트림에서 개방 데이터 마이닝 기반의 빈발항목 탐색,' 정보처리학회 논문지 D 제10-D권 제 3호, pp.447-458, 2003   과학기술학회마을   DOI
8 한승철, 강현철, 'XML 스트림 데이터에 대한 연속 질의 처리 시스템,' 정보처리학회 논문지 D 제11-D권 제7호, pp.1375-1384, 2004   과학기술학회마을   DOI
9 H. Han, H. Ryoo, and H. Patrick, 'An Infrastructure of Stream Data Mining, Fusion and Management for Monitored Patients,' In Proc. of 19th IEEE International Symposium on CBMS 2006, pp.461-468, June, 2006   DOI
10 K. Kuramitsu, 'Finding Periodic Outliers over a Monogenetic Event System,' In Proc. of UDM05, pp.97-104, April, 2005   DOI
11 G. S. Manku and R. Motwani, 'Approximate Frequency Counts over Data Streams,' In Proc. of VLDB, pp.346-357, 2002
12 R. Agrawal and R. Srikant, 'Fast Algorithms for mining association rule,' In Proc. of VLDB, Sep., 1994
13 G. Chen, X. Wu, and X. Zhu, 'Mining Sequential Patterns Across Data Streams,' Univ. of Vermont Computer Science Technical Report(CS-05-04), March, 2005
14 J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M. Hsu, 'Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach,' IEEE Transactions on Knowledge and Data Engineering, Vol.16, No.11, Nov., 2004   DOI   ScienceOn
15 R. C. Oliver, K. Smettem, M. Kran, and K. Mayer, 'Fielding Testing a Wireless Sensor Network for Reactive,' In Proc. of ISSNIP, pp.7-12, Dec., 2004