Centroid based clustering approaches, such as k-means, are relatively fast but inaccurate for arbitrary shape clusters. Fuzzy c-means with Mahalanobis distance can accurately identify clusters if data set be modelled by a mixture of Gaussian distributions. However, they require number apriori and bad initialization cause poor results. Density methods, DBSCAN, overcome these disadvantages. may p...
