Correlation Clustering

The social network can be viewed as a chromatic graph of relations and entities. Thus, the community discovery in social network is essentially a problem of chromatic correlation clustering. This paper aims to develop metrics to measure the performance of community discovery algorithms in view of nonoverlapping strong communities. Three performance metrics, namely, chromatic density (CD), chromatic cut ratio (CCR) and chromatic conductance (CC), were proposed for thorough analysis on the output quality of chromatic clustering algorithms. In addition, synthetic graph generator was developed to generate sparse networks with few dense and strong communities. Five algorithms for chromatic correlation clustering, i.e. CB, ICB, LCB, OCB and RECB, were evaluated by the proposed metrics. The evaluation shows that the RECB is the most suitable algorithm for the discovery of strong communities in social network. The research results shed important new light on the detection of communities in social network.

I analyzed different clustering methods of credit card customers data by some machine learning clustering algorithm i approached to classify the high dimensional data by using clustering algorithms in the real world ,It is a type of process where we divide the similar objects into one group The Objective of paper is to identify which method gives a general view of clustering techniques like K-means, algometrive and gaussian distribution ,to analyze Segmentation of customers can be used to define marketing strategies. Clustering comes under unsupervised learning, if we take data set it contains some input patterns, entities etc. The main aim of clustering is to make partition & to make clusters having similarity, so that we can get some useful insights and make further analyses .The process of finding closely connected and fair information from high dimensional datasets is complex and difficult to carry .In this paper we tried to analyze our model through different clustering models ...

Today clustering-based machine learning algorithms are the important field in data mining. Here, medical data clustering is one of the core applications of data mining to predict and identify the risk factor of the disease. At the same time, medical data clustering is a very important and challenging task due to its complexity and high frequency of data. In order to achieve proper data clustering, this paper proposed a hybrid data clustering algorithm by the combination of [Formula: see text]-Means and Black Hole Entropic Fuzzy Clustering (BHEFC). [Formula: see text]-Means is the first and one of the most popular and low-computation cost partitioned-based clustering algorithms. There are two modules in this hybrid clustering, first some number of iterations are executed by the first module of this hybrid clustering algorithm, which is [Formula: see text]-Means clustering. After some number of iterations, the clustering solutions are shifted to the second module of this hybrid cluste...

Data clustering is a process of partitioning data points into meaningful clusters such that a cluster holds similar data and different clusters hold dissimilar data. It is an unsupervised approach to classify data into different patterns. In general, the clustering algorithms can be classified into the following two categories: firstly, hard clustering, where a data object can belong to a single and distinct cluster and secondly, soft clustering, where a data object can belong to different clusters. In this report we have made a comparative study of three major data clustering algorithms highlighting their merits and demerits. These algorithms are: k-means, fuzzy cmeans and K-NN clustering algorithm. Choosing an appropriate clustering algorithm for grouping the data takes various factors into account for illustration one is the size of data to be partitioned.

In k-means clustering, we are given a set of n data points in d-dimensional space ℝd and an integer k and the problem is to determine a set of k points in ℝd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient