Density-based data streams subspace clustering over weighted sliding windows

Jiadong Ren; Shiyuan Cao; Changzhen Hu

doi:10.1109/CDEE.2010.48

Density-based data streams subspace clustering over weighted sliding windows

Jiadong Ren^*, Shiyuan Cao, Changzhen Hu

^*Corresponding author for this work

School of Cyberspace Science and Technology

Yanshan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Most real-world data sets are characterized by a high dimensinal, inherely sparse data space. In this paper, we present a novel density-based approach to the subspace clustering problem. A new framework for data stream mining is introduced, called the weighted sliding window. In the online component, the structure of Exponential Histogram of Cluster Feature(EHCF) is improved to maintain the micro-clusters. The concepts of potential core-micro-cluster and outlier micro-cluster are applied to distinguish the potential clusters and outliers. A novel pruning strategy is proposed to decrease the number of micro-clusters. In the offline component, the final clusters are generated by SUBCLU algorithm. Our performance study demonstrates the effectiveness and efficiency of our algorithm.

Original language	English
Title of host publication	Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010
Publisher	IEEE Computer Society
Pages	212-216
Number of pages	5
ISBN (Print)	9780769543321
DOIs	https://doi.org/10.1109/CDEE.2010.48
Publication status	Published - 2010

Publication series

Name	Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010

Keywords

Data stream
Density-based
Subspace clustering
Weighted sliding windows

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10.1109/CDEE.2010.48

Cite this

Ren, J., Cao, S., & Hu, C. (2010). Density-based data streams subspace clustering over weighted sliding windows. In Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010 (pp. 212-216). Article 5759375 (Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010). IEEE Computer Society. https://doi.org/10.1109/CDEE.2010.48

Ren, Jiadong ; Cao, Shiyuan ; Hu, Changzhen. / Density-based data streams subspace clustering over weighted sliding windows. Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010. IEEE Computer Society, 2010. pp. 212-216 (Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010).

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abstract = "Most real-world data sets are characterized by a high dimensinal, inherely sparse data space. In this paper, we present a novel density-based approach to the subspace clustering problem. A new framework for data stream mining is introduced, called the weighted sliding window. In the online component, the structure of Exponential Histogram of Cluster Feature(EHCF) is improved to maintain the micro-clusters. The concepts of potential core-micro-cluster and outlier micro-cluster are applied to distinguish the potential clusters and outliers. A novel pruning strategy is proposed to decrease the number of micro-clusters. In the offline component, the final clusters are generated by SUBCLU algorithm. Our performance study demonstrates the effectiveness and efficiency of our algorithm.",

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Ren, J, Cao, S & Hu, C 2010, Density-based data streams subspace clustering over weighted sliding windows. in Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010., 5759375, Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010, IEEE Computer Society, pp. 212-216. https://doi.org/10.1109/CDEE.2010.48

Density-based data streams subspace clustering over weighted sliding windows. / Ren, Jiadong; Cao, Shiyuan; Hu, Changzhen.
Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010. IEEE Computer Society, 2010. p. 212-216 5759375 (Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Most real-world data sets are characterized by a high dimensinal, inherely sparse data space. In this paper, we present a novel density-based approach to the subspace clustering problem. A new framework for data stream mining is introduced, called the weighted sliding window. In the online component, the structure of Exponential Histogram of Cluster Feature(EHCF) is improved to maintain the micro-clusters. The concepts of potential core-micro-cluster and outlier micro-cluster are applied to distinguish the potential clusters and outliers. A novel pruning strategy is proposed to decrease the number of micro-clusters. In the offline component, the final clusters are generated by SUBCLU algorithm. Our performance study demonstrates the effectiveness and efficiency of our algorithm.

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Ren J, Cao S, Hu C. Density-based data streams subspace clustering over weighted sliding windows. In Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010. IEEE Computer Society. 2010. p. 212-216. 5759375. (Proceedings - 2010 1st ACIS International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and Its Applications, and Embedded Systems, CDEE 2010). doi: 10.1109/CDEE.2010.48

Density-based data streams subspace clustering over weighted sliding windows

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