K-Means Clustering For Segment Web Search Results

Abstract

Clustering is the power full technique for segment relevant data into different levels. This study has proposed K- means clustering method for cluster web search results for search engines. For represent documents we used vector space model and use cosine similarity method for measure similarity between user query and the search results. As an improvement of K-means clustering we used distortion curve method for identify optimal initial number of clusters. should exhibit a large degree of similarity while the similarity among different clusters should be minimized. Some of the more familiar clustering methods are: partitioning algorithms based on dividing entire data into dissimilar groups, hierarchical methods, density and grid based clustering, some graph based methods, etc (3). Most popular and widely use partitioning method is K-means clustering method. A. K-means Clustering K-Means Clustering is a method used to classify semi structured or unstructured data sets. This is one of the most commonly and effective methods to classify data because of its simplicity and ability to handle voluminous data sets. It accepts the number of clusters and the initial set of centroids as parameters. The distance of each item in the data set is calculated with each of the centroids of the respective cluster. The item is then assigned to the cluster with which the distance of the item is the least. The centroid of the cluster to which the item was assigned is recalculated. One of the most important and commonly used methods for grouping the items of a data set using K-Means Clustering is calculating the distance of the point from the chosen mean. This distance is usually the Euclidean Distance. ∑ √( )