local search algorithm, data clustering, K-means, clustering aggregated data, clustering large data sets, data compression, vector quantization Data clustering is one of the common techniques used in data mining. A popular performance function for measuring goodness of the K-clustering is the total within-cluster variance, or the total mean-square quantization error (MSE). The K-Means (KM) algo...

Stochastic nature of earthquake has raised a challenge for engineers to choose which record for their analyses. Clustering is offered as a solution for such a data mining problem to automatically distinguish between ground motion records based on similarities in the corresponding seismic attributes. The present work formulates an optimization problem to seek for the best clustering measures. In...

In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...

a well-known algorithm of clustering is k-means by which the data are divided into k classes based upon a distance criterion. in the present research, by using k-means method for classifying data derived from exploration boreholes in the parkam deposit, the optimum k has been calculated and then the data have been clustered and the relative geochemical behavioral characteristics analyzed. the c...

V.Leela#1, K.Sakthi priya*2,R.Manikandan#3 #1M.tech VLSI Design, Department of Computing, SASTRA university,Thanjavur-613401,India. Email:[email protected] *2 M.tech VLSI Design, Department of Computing, SASTRA University,Thanjavur-613401,India. Email:[email protected] #3Senior Assistant Professor, Department of ICT,SASTRA University,Thanjavur-613401,India. Email:[email protected]...

The intelligent LINEX k-means clustering is a generalization of the k-means clustering so that the number of clusters and their related centroid can be determined while the LINEX loss function is considered as the dissimilarity measure. Therefore, the selection of the centers in each cluster is not randomly. Choosing the LINEX dissimilarity measure helps the researcher to overestimate or undere...

The K-means clustering algorithm is an old algorithm that has been intensely researched owing to its simplicity of implementation. However, there have also been criticisms on its performance, in particular, for demanding the value of K a priori. It is evident from previous researches that providing the number of clusters a priori does not in any way assist in the production of good quality clus...

Clustering is the unsupervised learning problem. Better Clustering improves accuracy of search results and helps to reduce the retrieval time. Clustering dispersion known as entropy which is the disorderness that occur after retrieving search result. It can be reduced by combining clustering algorithm with the classifier. Clustering with weighted k-mean results in unlabelled data. This paper pr...

In K-mean algorithm, every pixel in super space is required to calculate Euclidean distance for clustering, so it is one time-consuming hard work when there are a great many class centers. Improved K-mean clustering algorithm presented here can save clustering time by making initial division based on previous clustering results, and maintaining the relationship among stable classes during clust...

Due to fast growth of the internet technology there is need to establish security mechanism. So for achieving this objective NIDS is used. Datamining is one of the most effective techniques used for intrusion detection. This work evaluates the performance of unsupervised learning techniques over benchmark intrusion detection datasets. The model generation is computation intensive, hence to redu...