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Improved K-means algorithm for searching research papers

So we are proposed Enhanced architecture with improved kmeans algorithm, which proposes a method for making the algorithm more effective and efficient, so as to get better clustering with reduced complexity. It will search the base keyword of the content from the knowledge database. Proposed work uses the search engine based on clustering and text mining. | ISSN:2249-5789 Sachin Shinde et al , International Journal of Computer Science & Communication Networks,Vol 4(6),197-202 Improved K-means Algorithm for Searching Research Papers Sachin Shinde Department of Computer Engineering Flora institute of Technology, Pune Maharashtra, India Sachinsss2986@gmail.com Bharat Tidke Department of Computer Engineering Flora institute of Technology, Pune Maharashtra, India Sachinsss2986@gmail.com Abstract Clustering is one of the unsupervised learning method in which a set of essentials is separated into uniform groups. The k-means method is one of the most widely used clustering techniques for various applications. For the Searching as well as reading research papers users need more time or users spend two to three hours for searching or reading single papers, so this is more consuming process, so it is required that use enhanced search engine which is based on fastest reading algorithm which provides best output or results. So we are proposed Enhanced architecture with improved kmeans algorithm, which proposes a method for making the algorithm more effective and efficient, so as to get better clustering with reduced complexity. It will search the base keyword of the content from the knowledge database. Proposed work uses the search engine based on clustering and text mining. Keywords-Text mining, Clustering, K-means Algorithm, Enhanced K-means Algorithm. 1. Introduction Data mining, a synonym to “knowledge discovery in databases” is a process of Analyzing data from different perspectives and summarizing it into useful information. Clustering [2] is useful technique for the discovery of data distribution and patterns in the underlying data. Clustering is an example of unsupervised classification. Classification refers to a procedure that assigns data objects to a set of classes. Unsupervised Classifications means that clustering does not depend on predefined classes and no external teacher set is used. . The use of search .