Multilink Constrained k-means Clustering Algorithm for Information Retrieval

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-13 Number-3
Year of Publication : 2014
Authors : M.Parvathavarthini , E.Ramaraj


M.Parvathavarthini , E.Ramaraj. "Multilink Constrained k-means Clustering Algorithm for Information Retrieval", International Journal of Engineering Trends and Technology (IJETT), V13(3),140-143 July 2014. ISSN:2231-5381. published by seventh sense research group


Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, the popular k-means clustering algorithm can be profitably modified to make use of information with available instances is demonstrated. We can also apply this method to the real-world applications such as University database, hospital database etc. for information retrieval. In this proposed method the University data are collected to perform the k-means clustering algorithm to information retrieval. Information retrieval is the activity of obtaining information resources relevant to an information need from a collection of information resources. Many universities and public libraries use IR systems to provide access to books, journals and other documents. An information retrieval process begins when a user enters a query into the system.


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Clustering, Information Retrieval, k-means algorithm, Database.