This paper analyzes sensitivity of Fuzzy C-means to noisy which generates unreasonable clustering results. We also find that Fuzzy C-means possess monotonicity, which may generate meaningless clustering results. Aiming at these weak points, we present an improved Fuzzy C-means named IFCM (Improved Fuzzy C-means). Firstly, we research the reason of sensitivity and find that constraint leads to s...

With the rapid advances of microarray technologies, large amounts of high-dimensional gene expression data are being generated, which poses significant computational challenges. A first step towards addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. A ...

This work proposes a load balance algorithm to parallel processing based on a variation of the classical knapsack problem. The problem considers the distribution of a set of partitions, defined by the number of clusters, over a set of processors attempting to achieve a minimal overall processing cost. The work is an optimization for the parallel fuzzy c-means (FCM) clustering analysis algorithm...

In this paper proposes different conventional and fuzzy based clustering techniques for fault detection and isolation in process plant monitoring. Process plant monitoring is very important aspect to improve productiveness and efficiency of the product and plant. This paper takes a case study of plant data and implements K means algorithm and fuzzy C means algorithm to cluster the relevant data...

Clustering technique is one of the most important research areas in the field of data mining. This paper proposes an improved K-Means clustering algorithm form partition based clustering algorithms. It determines the initial centroid of the cluster and gives more efficient performance and reduces the time complexity of the large data sets. The data set used here is banking data. Fuzzy C-Means c...

Abstract-This paper presents a general approach to fuzzy clustering methods. A generalised fuzzy objective function is used to combine fuzzy c-means clustering, fuzzy entropy clustering, and their extended versions into a generalised fuzzy clustering method. Some new extended versions of the above-mentioned clustering methods are proposed from this general approach. Several cluster data sets we...

A genetic approach is developed, which is suitable for the optimization of fuzzy c-means clustering. The approach is based on real encoding of the prototype variables (cluster centers) and uses appropriate genetic operators and techniques to optimize the clustering criterion. Experimental results concerning diicult clustering problems show that the proposed approach is very successful in genera...

We present a consensus-based algorithm to distributed fuzzy clustering that allows automatic estimation of the number of clusters. Also, a variant of the parallel Fuzzy c-Means algorithm that is capable of estimating the number of clusters is introduced. This variant, named DFCM, is applied for clustering data distributed across different data sites. DFCM makes use of a new, distributed version...

This paper describes a hybrid approach of Fuzzy C-means clustering and Genetic Algorithm (GA) is proposed that provides better accuracy & increases the intrusion detection rate. This approach provides better accuracy of detection as compared to K-means and FCM Clustering. With this proposed approach intrusion detection rate is improved considerably.A brief overview of a hybrid approach of genet...