DocumentCode :
145221
Title :
Dynamic Incremental K-means Clustering
Author :
Aaron, Bryant ; Tamir, Dan E. ; Rishe, Naphtali D. ; Kandel, Abraham
Author_Institution :
Dept. of Comput. Sci., Texas State Univ., San Marcos, TX, USA
Volume :
1
fYear :
2014
fDate :
10-13 March 2014
Firstpage :
308
Lastpage :
313
Abstract :
K-means clustering is one of the most commonly used methods for classification and data-mining. When the amount of data to be clustered is "huge," and/or when data becomes available in increments, one has to devise incremental K-means procedures. Current research on incremental clustering does not address several of the specific problems of incremental K-means including the seeding problem, sensitivity of the algorithm to the order of the data, and the number of clusters. In this paper we present static and dynamic single-pass incremental K-means procedures that overcome these limitations.
Keywords :
data mining; pattern classification; pattern clustering; algorithm sensitivity; data classification; data mining; dynamic incremental k-means clustering; dynamic single-pass incremental K-means procedures; seeding problem; static single-pass incremental K-means procedures; Classification algorithms; Clustering algorithms; Convergence; Heuristic algorithms; Image color analysis; Quantization (signal); Vectors; Clustering; Data-mining; Incremental Clustering; K-means Clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Science and Computational Intelligence (CSCI), 2014 International Conference on
Conference_Location :
Las Vegas, NV
Type :
conf
DOI :
10.1109/CSCI.2014.60
Filename :
6822127
Link To Document :
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