Improved Rough Fuzzy C-Means Clustering Algorithm by Applying Decision Theory Using Syntactic Data

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
Clustering is a widely used technique in data mining application for discovering patterns in underlying data. Most traditional clustering algorithms are limited in handling datasets that contain attributes. In hard clustering data can be divided into crisp clusters, where each data point belongs to exactly one cluster. In rough clustering the data points can be belongs to more than one cluster, and associated with other cluster points. In this paper, the authors implement the crisp clustering, Rough clustering algorithm using the syntactic data and compare the analysis of the results. The goal of the paper gives the cluster evolution method using the decision theory.

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