2011•Computer Systems and ApplicationsRequires access

A Grid and MST Based Clustering Algorithm for Data Streams

Hai Wang

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Abstract

CluStream algorithm has poor quality of clustering for non-spherical clusters,at the same time,most grid-based clustering algorithms improve the efficiency of clustering at the cost of reducing clustering accuracy.The paper gives a new kind of clustering algorithm for data stream—GTSClu,it is the minimum spanning tree data stream clustering algorithm based on grid,which is divided into online processing and offline clustering,combining with grid resolution and minimum spanning tree techniques.GTSClu algorithm cannot only find clusters with arbitrary shape and amount,but also deal with noise data effectively,the efficiency and quality of clustering is improved.

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What this paper is about

CluStream algorithm has poor quality of clustering for non-spherical clusters,at the same time,most grid-based clustering algorithms improve the efficiency of clustering at the cost of reducing clustering accuracy.The paper gives a new kind of clustering algorithm for data stream—GTSClu,it is the minimum spanning tree data stream clustering algorithm based on grid,which is divided into online processing and offline clustering,combining with grid resolution and minimum spanning tree techniques.GTSClu algorithm cannot only find clusters with arbitrary shape and amount,but also deal with noise data effectively,the efficiency and quality of clustering is improved.

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Available abstract

CluStream algorithm has poor quality of clustering for non-spherical clusters,at the same time,most grid-based clustering algorithms improve the efficiency of clustering at the cost of reducing clustering accuracy.The paper gives a new kind of clustering algorithm for data stream—GTSClu,it is the minimum spanning tree data stream clustering algorithm based on grid,which is divided into online processing and offline clustering,combining with grid resolution and minimum spanning tree techniques.GTSClu algorithm cannot only find clusters with arbitrary shape and amount,but also deal with noise data effectively,the efficiency and quality of clustering is improved.

Key concepts: Computer science, Cluster analysis, CURE data clustering algorithm, Data stream clustering, Correlation clustering, Canopy clustering algorithm, Data mining, Determining the number of clusters in a data set

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