The study is conducted to propose a multi-step feature (term) selection process and in semi-supervised fashion, provide initial centers for term clusters. Then utilize the fuzzy c-means (FCM) clustering algorithm for clustering terms. Finally assign each of documents to closest associated term clusters. While most text clustering algorithms directly use documents for clustering, we propose to f...

Identifying specific CT-image regions is an important process in medical diagnosis. Clustering is a simple and useful means for automatic image segmentation. However, clustering results vary with the features of image pixels and the settings of parameters of the clustering methods. This study compares the results of CT image segmentation using FCMbased clustering algorithms running with intensi...

In web search applications, queries are submitted to search engines to represent the information needs of users. Discovering the number of diverse user search goals for a query and depicting each goal with some keywords automatically. In the existing work propose a novel approach to infer user search goals by analyzing search engine query logs. First propose a novel approach to infer user searc...

This paper describes a technique to overcome the sensitivity of fuzzy C-means clustering for unequal cluster sizes in multivariate images. As FCM tends to balance the number of points in each cluster, cluster centres of smaller clusters are drawn to larger adjacent clusters. In order to overcome this, a modified version of FCM, called Conditional FCM, is used to balance the different sized clus...

 Clustering is a challenging problem in data mining, requiring both accurate determination of the number of clusters and correct clustering of the data. Fuzzy C-means (FCM) is a popular algorithm using the partitioning approach to solve this problem. A drawback to FCM is that it requires the number of clusters to be set a priori. In this study, we combine FCM with Genetic Algorithm (GA), Subtr...

Fuzzy C-means (FCM) is a popular algorithm using the partitioning approach to solve problems in data clustering. A drawback to FCM, however, is that it requires the number of clusters and the clustering partition matrix to be set a priori. Typically, the former is set by the user and the latter is initialized randomly. This approach may cause the algorithm get stuck in a local optimum because F...

Telecom user behavior analysis, is that in the case of gaining the basic consumption data of the users to disposal, count, and analyze the relevant data, and discover the law of the users consumption from it, and combine these laws with the telecom marketing strategies to find the problems in the current marketing campaign which will provide the basis for the design of the scientific decision-m...

In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in “Big Data” where computation time is equally or more important than accuracy. However, there...

Clustering is a key process in data mining for revealing structure and patterns in data. Fuzzy C-means (FCM) is a popular algorithm using a partitioning approach for clustering. One advantage of FCM is that it converges rapidly. In addition, using fuzzy sets to represent the degrees of cluster membership of each data point provides more information regarding relationships within the data than d...

— Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. O...