JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 3    Issue : 12    Date : December 2008

Weighted Clustering and Evolutionary Analysis of Hybrid Attributes Data Streams
Xinquan Chen
Page(s): 60-67
Full Text:
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Abstract
It presents some definitions of projected cluster and projected cluster group on hybrid attributes
after having given some definitions on ordered attributes and sorted attributes to solve clustering
analysis problem of infinite hybrid attributes data streams in finite space. In order to improve the
clustering quality of hybrid attributes data streams, it presents a two-step projected clustering
method, which can often make better clustering effects in two simulated experiments although it is
very simple. At last, it gives a dividing and merging framework of infinite hybrid attributes data
streams. In order to implement this framework, it presents 8 properties in Section IV, some data
structure definitions and 15 algorithms in appendix. The framework is verified and these algorithms
are tested by German data set with a better clustering quality than WKMeans sometimes if having
set right parameters.

Index Terms
ordered attributes, sorted attributes, hybrid attributes, projected clustering, merging clusters,
subtracting of clusters, merging cluster groups, evolutionary analysis of cluster groups