Dynamic Incremental Fuzzy C-Means Clustering (original) (raw)

FCM : Fuzzy C-Means Clustering – A View in Different Aspects

Data Mining is the process of obtaining or exploring data from the large amount of raw data. It produces the meaningful information. To obtain the information data mining has multiple techniques such as classification, regression, prediction, clustering, and summarization. There are multiple tasks in data mining to obtain the information such as cleaning, integrating, selection, transformation, pattern evaluation. One of the challenging techniques in the data mining is clustering. Clustering is the process of grouping the data under some condition. The main aim of the paper is to describe about the Fuzzy C-Means Clustering (FCM) and compared with K-Means clustering. The pitfalls overcome by the FCM are also measured theoretically.

Fuzzy C-Mean Clustering Algorithm Modification and Adaptation for Applications

Many clustering algorithms with different methodologies are subjected to be common techniques and main step in many applications in the computer science world. The need of adapting efficient clustering algorithm increases in critical applications (i.e. wireless sensors networks). Utilizing the Fuzzy Logic power; Fuzzy C-mean (FCM) clustering has a major role in most clustering applications. But in many cases, the result of FCM is considered to be non-complete clustering strategy. This paper adapted the FCM algorithm to enable of generating clusters with equal sizes. Also, scattered points that are located far away from all clusters are grouped out of clusters. Another modification is to localize specific points that have ability to locate in more than one cluster; hence this has a non-negligible importance in some fields such as cellular communications

A New Kernelized Fuzzy C-Means Clustering Algorithm with Enhanced Performance

Recently Kernelized Fuzzy C-Means clustering technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like the conventional Fuzzy C-Means clustering technique this technique also suffers from inconsistency in its performance due to the fact that here also the initial centroids are obtained based on the randomly initialized membership values of the objects. Our present work proposes a modified method to remove the effect of random initialization from Kernelized Fuzzy C-Means clustering technique and to improve the overall performance of it. In our proposed method we have used the algorithm of Yuan et al. to determine the initial centroids. These initial centroids are then used in the conventional Kernelized Fuzzy C-Means clustering technique to obtain the final clusters. We have also provided a comparison of ou...

A multivariate Fuzzy C-Means method

Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.