A Comparative Study between of Fuzzy C-Means Algorithms and Density based Spatial Clustering of Applications with Noise
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Keywords:
Data Clustering Algorithm, Data Mining, DBSCAN, FCM, Fuzzy C-Means, K-meansAbstract
Data mining technology has emerged as a means of identifying patterns and trends from large amounts of data and is a computing intelligence area that provides tools for data analysis, new knowledge discovery, and autonomous decision making. Data clustering is an important problem in many areas. Fuzzy C-Means(FCM)[11,12,13] is a very important clustering technique based on fuzzy logic. DBSCAN(Density Based Spatial Clustering of Applications with Noise)[8] is a density-based clustering algorithm that is suitable for dealing with spatial data including noise and is a collection of arbitrary shapes and sizes. In this paper, we compare and analyze the performance of Fuzzy C-Means and DBSCAN algorithms in different data sets.
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