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http://dx.doi.org/10.3745/KIPSTB.2003.10B.3.297

The Study on Improvement of Cohesion of Clustering in Incremental Concept Learning  

Baek, Hey-Jung (숭실대학교 대학원 컴퓨터학과)
Park, Young-Tack (숭실대학교 컴퓨터학부)
Publication Information
The KIPS Transactions:PartB / v.10B, no.3, 2003 , pp. 297-304 More about this Journal
Abstract
Nowdays, with the explosive growth of the web information, web users Increase requests of systems which collect and analyze web pages that are relevant. The systems which were develop to solve the request were used clustering methods to improve the duality of information. Clustering is defining inter relationship of unordered data and grouping data systematically. The systems using clustering provide the grouped information to the users. So, they understand the information efficiently. We proposed a hybrid clustering method to cluster a large quantity of data efficiently. By that method, We generate initial clusters using COBWEB Algorithm and refine them using Ezioni Algorithm. This paper adds two ideas in prior hybrid clustering method to increment accuracy and efficiency of clusters. Firstly, we propose the clustering method considering weight of attributes of data. Second, we redefine evaluation functions which generate initial clusters to increase efficiency in clustering. Clustering method proposed in this paper processes a large quantity of data and diminish of dependancy on sequence of input of data. So the clusters are useful to make user profiles in high quality. Ultimately, we will show that the proposed clustering method outperforms the pervious clustering method in the aspect of precision and execution speed.
Keywords
Hybrid Clustering; Weight; Evaluation Function; COBWEB; Etzioni;
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