Clustering is the process of assigning a homogeneous group of objects into subsets called clusters, so that objects in each cluster are more similar to each other than objects from different clusters based on the values of their Receptor Tyrosine Kinase Signaling attributes [1]. Clustering techniques have been studied extensively in data mining [2], pattern recognition [3], and machine learning [4]. Clustering algorithms can be generally grouped into two main classes, namely, supervised clustering and unsupervised clustering where the parameters of classifier are optimized. Many unsupervised clustering algorithms

have been developed. One such algorithm is k-means, which assigns n objects to k clusters by minimizing the sum of squared Euclidean distance between the objects in each cluster to the cluster center. The main drawback of the k-means algorithm is that the result is sensitive to the selection of initial cluster centroids and may converge to local optima [5]. For handling those random distribution data sets, soft computing has been introduced in clustering [6],

which exploits the tolerance for imprecision and uncertainty in order to achieve tractability and robustness. Fuzzy sets and rough sets have been incorporated in the c-means framework to develop the fuzzy c-means (FCM) [7] and rough c-means (RCM) [8] algorithms. Fuzzy algorithms can assign data object partially to multiple clusters and handle overlapping partitions. The degree of membership in the fuzzy clusters depends on the closeness of the data object to the cluster centers. The most popular fuzzy clustering algorithm is FCM which is introduced by Bezdek [9] and now it is widely used. FCM is an effective algorithm, but the random selection in center points makes iterative process fall into the saddle points or local optimal solution easily. Furthermore,

if the data sets contain severe noise points or if the data sets are high dimensional, such as bioinformatics [10], the alternating optimization often fails to find the global optimum. In these cases, the probability of finding the global optimum can be increased by stochastic methods such as evolutionary or swarm-based methods. Bezdek and Hathaway [11] optimized the hard c-means (HCM) model with a genetic algorithm. Runkler [12] Entinostat introduced an ant colony optimization algorithm which explicitly minimizes the HCM and FCM cluster models. Al-Sultan and Selim [13] proposed the simulated annealing algorithm (SA) to overcome some of these limits and got promising results. PSO is a population based optimization tool developed by Eberhart and Kennedy [14], which can be implemented and applied easily to solve various function optimization problems. Runkler and Katz [15] introduced two new methods for minimizing the reformulated objective functions of the FCM clustering model by PSO: PSO-V and PSO-U.