In data mining clustering techniques are used to group together the objects showing similar characteristics within the same cluster and the objects demonstrating different characteristics are grouped into clusters. Clustering approaches can be classified into two categories namelyHard clustering and Soft clustering. In hard clustering data is divided into clusters in such a way that each data i...

The challenging issue in microarray technique is to analyze and interpret the large volume of data. This can be achieved by clustering techniques in data mining. In hard clustering like hierarchical and k-means clustering techniques, data is divided into distinct clusters, where each data element belongs to exactly one cluster so that the out come of the clustering may not be correct in many ti...

Clustering is a data mining technique of grouping set of data objects into multiple groups or clusters so that objects within the cluster have high similarity, but are very dissimilar to the objects in the other clusters. Fuzzy C-Means is the most widely used method where an element may have partial membership grades in more than one fuzzy cluster. This paper makes use of MATLAB language to pro...

Fuzzy C-means is a widely used clustering algorithm in data mining. Since traditional fuzzy C-means algorithms do not take spatial information into consideration, they often can’t effectively explore geographical data information. So in this paper, we design a Spatial Distance Weighted Fuzzy C-Means algorithm, named as SDWFCM, to deal with this problem. This algorithm can fully use spatial feat...

Fuzzy C-Mean (FCM) is an unsupervised clustering algorithm based on fuzzy set theory that allows an element to belong to more than one cluster. Where fuzzy means “unclear” or “not defined” and c denotes “clustering”. In FCM the number of cluster are randomly selected. [15] FCM is the advanced version of K-means clustering algorithm and doing more work than K-means. K-Means just needs to do a di...

This paper presents an algorithm, called the modified suppressed fuzzy c-means (MS-FCM), that simultaneously performs clustering and parameter selection for the suppressed fuzzy c-means (S-FCM) algorithm proposed by [Fan, J.L., Zhen, W.Z., Xie, W.X., 2003. Suppressed fuzzy c-means clustering algorithm. Pattern Recognition Lett. 24, 1607–1612]. The proposed algorithm is computationally simple, a...

Researchers have observed that multistage clustering can accelerate convergence and improve clustering quality. Two-stage and two-phase fuzzy C-means (FCM) algorithms have been reported. In this paper, we demonstrate that the FCM clustering algorithm can be improved by the use of static and dynamic single-pass incremental FCM procedures. Keywords-Clustering; Fuzzy C-Means Clustering; Incrementa...