2,094 research outputs found
A fuzzy semantic information retrieval system for transactional applications
In this paper, we present an information retrieval system based on the concept of fuzzy logic to relate vague and uncertain objects with un-sharp boundaries. The simple but comprehensive user interface of the system permits the entering of uncertain specifications in query forms. The system was modelled and simulated in a Matlab environment; its implementation was carried out using Borland C++ Builder. The result of the performance measure of the system using precision and recall rates is encouraging. Similarly, the smaller amount of more precise information retrieved by the system will positively impact the response time perceived by the users
A Graph-based approach for text query expansion using pseudo relevance feedback and association rules mining
Pseudo-relevance feedback is a query expansion approach whose terms are selected from a set of top ranked retrieved documents in response to the original query. However, the selected terms will not be related to the query if the top retrieved documents are irrelevant. As a result, retrieval performance for the expanded query is not improved, compared to the original one. This paper suggests the use of documents selected using Pseudo Relevance Feedback for generating association rules. Thus, an algorithm based on dominance relations is applied. Then the strong correlations between query and other terms are detected, and an oriented and weighted graph called Pseudo-Graph Feedback is constructed. This graph serves for expanding original queries by terms related semantically and selected by the user. The results of the experiments on Text Retrieval Conference (TREC) collection are very significant, and best results are achieved by the proposed approach compared to both the baseline system and an existing technique
Class Association Rules Mining based Rough Set Method
This paper investigates the mining of class association rules with rough set
approach. In data mining, an association occurs between two set of elements
when one element set happen together with another. A class association rule set
(CARs) is a subset of association rules with classes specified as their
consequences. We present an efficient algorithm for mining the finest class
rule set inspired form Apriori algorithm, where the support and confidence are
computed based on the elementary set of lower approximation included in the
property of rough set theory. Our proposed approach has been shown very
effective, where the rough set approach for class association discovery is much
simpler than the classic association method.Comment: 10 pages, 2 figure
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