The clustering is a without monitoring process and one of the most common data mining techniques. The purpose of clustering is grouping similar data together in a group, so were most similar to each other in a cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering partition k-means, due to ease of implementation and high-speed performance o...

A fuzzy if-then rule base may be viewed as a partial function between universes of fuzzy sets. We show how this partial function determines a total one by means of the method of smooting splines. To this end, we identify the fuzzy sets with elements of a finite-dimensional real parameter space in an approximate way, using Perfilieva’s fuzzy transforms.

Color quantization (reduction) is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. Recent studies have demonstrated the effectiveness of hard c-means (k-means) clustering algorithm in this domain. Other studies reported similar findings pertaining to the fuzzy c-means algorithm. Interes...

Today, e-commerce has occupied a large volume of economic exchanges. It is known as one of the most effective business practices. Predicted trust which means trusting an anonymous user is important in online communities. In this paper, the trust was predicted by combining two methods of multiplex network and community detection. In modeling the network in terms of a multiplex network, the relat...

Cluster analysis is concerned with partitioning cases into clusters such that the cases in a cluster are similar in terms of a set of variables. The K-Means Clustering Algorithm is a popular clustering method. It finds k clusters by choosing k data points as initial cluster centroids. Each data point is then assigned to the cluster with center that is closest to that point. In K-Means, the numb...

In this article, we explore the theoretical aspects of the expectation maximization algorithm and how it can be applied to estimation of data as a Gaussian mixture model. We form comparisons and show how by the simplification of some parameters we can form the heuristic k-means algorithm. We then demonstrate through the authors’ code several situations where the EM-GMM algorithm performs signif...

This paper develops the cascaded models for classification of PIMA Indian diabetes database. The k-nearest neighbour method is used to impute the missing data and the processed data is used for further classification. This is done in two steps, in first step k-means clustering algorithm is used for extracting hidden patterns in data set then in second step the classification is done by using su...

Clustering is a task of grouping data based on similarity. A popular k-means algorithm groups data by firstly assigning all data points to the closest clusters, then determining the cluster means. The algorithm repeats these two steps until it has converged. We propose a variation called weighted k-means to improve the clustering scalability. To speed up the clustering process, we develop the r...

Finding the collapse susceptible portion of a power system is one of the purposes of voltage stability analysis. This part which is a voltage control area is called the voltage weak area. Determining the weak area and adjecent voltage control areas has special importance in the improvement of voltage stability. Designing an on-line corrective control requires the voltage weak area to be determi...

Finding the collapse susceptible portion of a power system is one of the purposes of voltage stability analysis. This part which is a voltage control area is called the voltage weak area. Determining the weak area and adjecent voltage control areas has special importance in the improvement of voltage stability. Designing an on-line corrective control requires the voltage weak area to be determi...