Hybrid Fuzzy Data Clustering Algorithm Using Different Distance Metrics: A Comparative Study

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Provided by: International Journal of Soft Computing and Engineering (IJSCE)
Topic: Data Management
Format: PDF
Clustering is the process of grouping a set of objects into a number of clusters. K-means and Fuzzy C-Means (FCM) algorithm have been extensively used in cluster analysis. However, they are sensitive to noise and do not include any information about spatial context. A Penalized Fuzzy C-Means algorithm (PFCM) was developed to overcome the drawbacks of FCM algorithm. Euclidean distance measure is commonly used by many researchers in traditional clustering algorithms. In this paper, a comparative study on hybrid fuzzy data clustering algorithm using different distance metrics such as Euclidean, city block and chessboard is proposed.
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