Adaptive Data Mining Model using Fuzzy Performance Measures 


Vol. 13,  No. 5, pp. 541-546, Oct.  2006
10.3745/KIPSTB.2006.13.5.541


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  Abstract

Data Mining is the process of finding hidden patterns inside a large data set. Cluster analysis has been used as a popular technique for data mining. It is a fundamental process of data analysis and it has been playing an important role in solving many problems in pattern recognition and image processing. If fuzzy cluster analysis is to make a significant contribution to engineering applications, much more attention must be paid to fundamental decision on the number of clusters in data. It is related to cluster validity problem which is how well it has identified the structure that is present in the data. In this paper, we design an adaptive data mining model using fuzzy performance measures. It discovers clusters through an unsupervised neural network model based on a fuzzy objective function and evaluates clustering results by a fuzzy performance measure. We also present the experimental results on newsgroup data. They show that the proposed model can be used as a document classifier.

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[IEEE Style]

H. S. Rhee, "Adaptive Data Mining Model using Fuzzy Performance Measures," The KIPS Transactions:PartB , vol. 13, no. 5, pp. 541-546, 2006. DOI: 10.3745/KIPSTB.2006.13.5.541.

[ACM Style]

Hyun Sook Rhee. 2006. Adaptive Data Mining Model using Fuzzy Performance Measures. The KIPS Transactions:PartB , 13, 5, (2006), 541-546. DOI: 10.3745/KIPSTB.2006.13.5.541.

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