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 this paper, we present a system based on an appropriately targeted version of the well-known fuzzy C-means (FCM) algorithm to determine a small number of profiles of typical Web site users from the Web access log. These profiles can be extremely useful, for instance, to customize the Web site, or to send personalized advertisements. After filtering the access log, for instance, by eliminatin...

Gaussian mixture model or Gaussian mixture density model(GMM) uses the likelihood function as a measure of fit. We show that just the same algorithm as the GMM can be derived from a modified objective function of Fuzzy c-Means (FCM) clustering with the regularizer by K-L information, only when the parameter λ equals 2. Although the fixed-point iteration scheme of FCM is similar to that of the G...

We introduce a new category of fuzzy neural networks with multiple-output based on fuzzy clustering algorithm, especially, fuzzy c-means clustering algorithm (FCM-based FNNm) for pattern classification in this paper. The premise part of the rules of the proposed networks is realized with the aid of the scatter partition of input space generated by FCM clustering algorithm. The partitioned local...

The problem of clustering a real s-dimensional data set X={x(1 ),,,,,x(n)} subset R(s) is considered. Usually, each observation (or datum) consists of numerical values for all s features (such as height, length, etc.), but sometimes data sets can contain vectors that are missing one or more of the feature values. For example, a particular datum x(k) might be incomplete, having the form x(k)=(25...

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...

This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer...

— 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...

In this research we design a network administrator assistance system based on traffic measurement and fuzzy c-means (FCM) clustering analysis method. Network traffic measurement is an essential tool for monitoring and controlling communication network. It can provide valuable information about network trafficload patterns and performances. The proposed system utilizes the FCM method to analyze ...

Estimation of temporal fuzzy sets that model dynamic processes is discussed. It has been found that although poles of attraction can be estimated fairly well with different fuzzy partitioning algorithms, membership function estimates may fail in accurately describing dynamic changes within the observed signals. Two types of fuzzy partitioning algorithms are compared: fuzzy c-means (FCM) and fuz...