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Case retrieval optimization of Case-based reasoning through Knowledge-intensive Similarity measures

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International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 34 - Number 3
Year of Publication: 2011
Authors:
Surjeet Dalal
Dr. Vijay Athavale
Keshav Jindal
10.5120/4078-5872

Surjeet Dalal, Dr. Vijay Athavale and Keshav Jindal. Article: Case retrieval optimization of Case-based reasoning through Knowledge-intensive Similarity measures. International Journal of Computer Applications 34(3):12-18, November 2011. Full text available. BibTeX

@article{key:article,
	author = {Surjeet Dalal and Dr. Vijay Athavale and Keshav Jindal},
	title = {Article: Case retrieval optimization of Case-based reasoning through Knowledge-intensive Similarity measures},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {34},
	number = {3},
	pages = {12-18},
	month = {November},
	note = {Full text available}
}

Abstract

Case based reasoning has become the emerging field of Artificial Intelligence area. It is mostly used in designing the real time application having the decision support capability. It reassembles with human reasoning approach. This reasoning approach contains four phases. It stores the solution of past problems faced in form the case in its case base. In this paper we have discussed about the case retrieval phase of case based reasoning approach. All efficiency of the CBR system depends on the case retrieval process. There are various strategies are used in this phase of case based reasoning. Nearest neighbour & Induction retrieval algorithms are discussed. These algorithms are very simple but inefficient in larger case base & incomplete case. In this paper we will discuss Knowledge-Intensive Similarity measure retrieval strategies for the case base reasoning system & model the knowlededge-intensive similarity measure by using myCBR tool. The basic purpose of our work is to over the bottlenecks of other retrieval strategies.

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