DocumentCode :
3093928
Title :
K-means: Clustering by Gradual Data Transformation
Author :
Malinen, Mikko ; Fränti, Pasi
Author_Institution :
Sch. of Comput., Univ. of Eastern Finland, Joensuu, Finland
fYear :
2011
fDate :
12-15 Aug. 2011
Firstpage :
350
Lastpage :
355
Abstract :
Traditional approach to clustering is to fit a model (partition or prototypes) for the given data. We propose a completely opposite approach by fitting the data into a given clustering model that is optimal for similar pathological data of equal size and dimensions. We then perform inverse transform from this synthetic data back to the original data while refining the optimal clustering structure during the process. The key idea is that we do not need to find optimal global allocation of the prototypes. Instead, we only need to perform local fine-tuning of the clustering prototypes during the transformation in order to preserve the already optimal clustering structure.
Keywords :
data handling; inverse transforms; pattern clustering; clustering model; clustering prototype fine-tuning; gradual data transformation; inverse transform; k-means clustering; optimal clustering structure; optimal global allocation; pathological data; Clustering algorithms; Complexity theory; Mean square error methods; Prototypes; Resource management; Streaming media; Transforms; Clustering; K-means; Mean Square Error;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Graphics (ICIG), 2011 Sixth International Conference on
Conference_Location :
Hefei, Anhui
Print_ISBN :
978-1-4577-1560-0
Electronic_ISBN :
978-0-7695-4541-7
Type :
conf
DOI :
10.1109/ICIG.2011.73
Filename :
6005585
Link To Document :
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