Improved Membership Function for Multiclass Clustering with Fuzzy Rule Based Clustering Approach

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Provided by: International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Topic: Data Management
Format: PDF
Fuzzy clustering is the combination of clustering and fuzzy set theory. It is useful to handle the problem of determining the vague boundaries of clusters. Fuzzy clustering is better than crisp clustering when the boundaries between the clusters are vague and ambiguous. In both fuzzy and crisp clustering algorithms there is need and requirement to know the number of potential clusters and/or their initial positions in advance. The existing system identifies the potential clusters in given dataset by itself. It uses the fuzzy rules for identifying the potential clusters.
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