An Efficient Difficult Keyword Prediction using Clustered Based Model View Similarity Matrix

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International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2017 by IJCOT Journal
Volume - 8 Issue - 1
Year of Publication : 2018
AuthorsPalakurthi Sahitya, E. Deepthi

MLA

Palakurthi Sahitya, E. Deepthi "An Efficient Difficult Keyword Prediction using Clustered Based Model View Similarity Matrix", International Journal of Computer & organization Trends (IJCOT), V8(1):19-23 January - February  2018, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract
To the best of our knowledge, there has not been any work on predicting or analysing the difficulties of queries over databases. Researchers have proposed some methods to detect difficult queries over plain text document collections. However, these techniques are not applicable to our problem since they ignore the structure of the database. In particular, as mentioned earlier, a Keyword query interface must assign each query term to a schema element in the database. It must also distinguish the desired result type. We empirically show that direct adaptations of these techniques are ineffective for structured data. In this paper we are propose topic based cluster search algorithm for search of keyword in the database. By implementing this technique we can improve more efficiency of query oriented keyword search.

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Keywords
Keyword, Clustering Data Mining, Query Searching, Difficult Keyword.