Organizing data into semantically more meaningful is one of the fundamental modes of understanding and learning. Cluster analysis is a formal study of methods for understanding and algorithm for learning. K-mean clustering algorithm is one of the most fundamental and simple clustering algorithms. When there is no prior knowledge about the distribution of data sets, K-mean is the first choice fo...

Organizing data into semantically more meaningful is one of the fundamental modes of understanding and learning. Cluster analysis is a formal study of methods for understanding and algorithm for learning. K-mean clustering algorithm is one of the most fundamental and simple clustering algorithms. When there is no prior knowledge about the distribution of data sets, K-mean is the first choice fo...

Cluster analysis is a useful technique in multivariate statistical analysis. Different types of hierarchical cluster analysis and K-means have been used for data analysis in previous studies. However, the K-means algorithm can be improved using some metaheuristics algorithms. In this study, we propose simulated annealing based algorithm for K-means in the clustering analysis which we refer it a...

Crime detection is one of the major issues in the field of criminology. In fact, criminology includes knowing the details of a crime and its intangible relations with the offender. In spite of the enormous amount of data on offenses and offenders, and the complex and intangible semantic relationships between this information, criminology has become one of the most important areas in the field o...

We present a k-means-based clustering algorithm, which optimizes mean square error, for given cluster sizes. A straightforward application is balanced clustering, where the sizes of each cluster are equal. In k-means assignment phase, the algorithm solves the assignment problem by Hungarian algorithm. This is a novel approach, and makes the assignment phase time complexity O(n), which is faster...

This paper shows how it made possible in geographical science to observe the seismic zone, clustering of highly sensitive earthquake zone and spatial data clustering during important geographical processes. This paper shows simple density based and KMean clustering technique. Density-Based clustering is done here using density estimation and by searching regions which are denser than a given th...

clustering is the process of dividing a set of input data into a number of subgroups. the members of each subgroup are similar to each other but different from members of other subgroups. the genetic algorithm has enjoyed many applications in clustering data. one of these applications is the clustering of images. the problem with the earlier methods used in clustering images was in selecting in...

K-means clustering algorithms are widely used for many practical applications. Original k-mean algorithm select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it generates unstable and empty clusters which are meaningless. The original k-means algorithm is computationally expensive and requires time proportional to the product of the numbe...

The main aspiration of Grid Computing is to aggregate the maximum available idle computing power of the distributed resources, and provide well-organized services to users. An efficient grid resource allocation is very much essential to achieve this aspiration. Researchers had proposed many grid scheduling mechanisms in the past to fulfill this aspiration, but, these are mostly based on traditi...