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    STUDIA INFORMATICA - Issue no. 1 / 2005  
         
  Article:   CORE BASED INCREMENTAL CLUSTERING.

Authors:  GABRIELA ŞERBAN, ALINA CÂMPAN. 		 
       
         
  Abstract:  Clustering is a data mining activity that aims to differentiate groups inside a given set of objects, with respect to a set of relevant at- tributes of the analyzed objects. Generally, existing clustering methods, such as k-means algorithm, start with a known set of objects, measured against a known set of attributes. But there are numerous applications where the attribute set characterizing the objects evolves. We propose in this paper an incremental, k-means based clustering method, Core Based Incremental Clustering (CBIC), that is capable to re-partition the objects set,when the attributes set increases. The method starts from the partitioning into clusters that was established by applying k-means or CBIC before the attribute set changed. The result is reached more eficiently than running k-means again from the scratch on the feature-extended object set. Experiments proving the method''s eciency are also reported.  
         
     
         
         
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