2009•Journal of Southwest Jiaotong UniversityRequires access

Effective Twice-Clustering Algorithm for Data Streams

Gongqing Wu

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Abstract

In order to enhance the quality of data stream clustering towards noisy and unbalanced data,an effective twice-clustering algorithm for data streams,TCLUSA for short,was proposed.TCLUSA is based on the simple divide-and-conquer and separability theorems,uses DBSCAN(density-based spatial clustering of applications with noise) to get the average point of each cluster as its local result,and then achieves the final result by clustering all the average points using the k-means.The algorithm keeps all the average points by a layered structure.The theoretical analysis and experimental results demonstrate that the proposed algorithm can enhance clustering quality efficiently when data distribution is abnormal or a high dimensional data stream is dealt with.

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What this paper is about

In order to enhance the quality of data stream clustering towards noisy and unbalanced data,an effective twice-clustering algorithm for data streams,TCLUSA for short,was proposed.TCLUSA is based on the simple divide-and-conquer and separability theorems,uses DBSCAN(density-based spatial clustering of applications with noise) to get the average point of each cluster as its local result,and then achieves the final result by clustering all the average points using the k-means.The algorithm keeps all the average points by a layered structure.The theoretical analysis and experimental results demonstrate that the proposed algorithm can enhance clustering quality efficiently when data distribution is abnormal or a high dimensional data stream is dealt with.

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Available abstract

In order to enhance the quality of data stream clustering towards noisy and unbalanced data,an effective twice-clustering algorithm for data streams,TCLUSA for short,was proposed.TCLUSA is based on the simple divide-and-conquer and separability theorems,uses DBSCAN(density-based spatial clustering of applications with noise) to get the average point of each cluster as its local result,and then achieves the final result by clustering all the average points using the k-means.The algorithm keeps all the average points by a layered structure.The theoretical analysis and experimental results demonstrate that the proposed algorithm can enhance clustering quality efficiently when data distribution is abnormal or a high dimensional data stream is dealt with.

Key concepts: Cluster analysis, DBSCAN, Data stream clustering, CURE data clustering algorithm, Computer science, Canopy clustering algorithm, Correlation clustering, Data stream mining

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