Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximu...

K-Means is probably the leading clustering algorithm with several applications in varying fields such as image processing and patterns analysis. K-Means has been the basis for several clustering algorithms including Kernel KMeans. In machine intelligence and related domains Kernelization transforms the data into a higher dimensional feature space by calculating the inner products between the di...

Several large scale data mining applications, such as text categorization and gene expression analysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a g...

The emergence of modern technology has enforced to collect the scientific data in a large quantity and those data are getting amassed in different databases. An organized analysis of data is very essential to obtain useful information from swiftly growing data repositories. Cluster analysis is one of the major data mining methods and the k-means clustering algorithm is widely used for many prac...

This paper proposes a novel algorithm of hierarchical divisive clustering, which generates a multi-branch tree, not a binary one, as its output. In order to use the algorithm for clustering large document sets, a spherical kmeans clustering algorithm based on a cosine measure is adopted for partitioning recursively the document set from the top to bottom. Also, by selecting automatically the nu...

In our work an efficient methodology is implemented to improve the accuracy of search results from the web databases on various keywords such as movies, CD, books etc. The improvement of Alignment algorithm using K-means clustering is proposed for the searching of annotated results from the web databases. The technique implemented is for the proficient retrieval of text nodes and data units usi...

Data mining is the process of extracting hidden patterns from huge data. Among the various clustering algorithms, k-means is the one of most widely used clustering technique in data mining. The performance of k-means clustering depends on the initial clusters and might converge to local optimum. K-means does not guarantee the unique clustering because it generates different results with randoml...

Document clustering, one of the traditional data mining techniques, is an unsupervised learning paradigm where clustering methods try to identify inherent grouping of the text documents.The importance of document clustering emerges from the massive volumes of textual documents created. Also, with more and more development of information technology, data set in many domains is reaching beyond pe...