A case retrieval approach using similarity and association knowledge

Authors:
Yong-Bin Kang;Shonali Krishnaswamy;Arkady Zaslavsky
Affiliations:
Faculty of IT, Monash University, Australia;Faculty of IT, Monash University, Australia;Faculty of IT, Monash University, Australia and ICT Centre, CSIRO, Australia
Venue:
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Year:
2011

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Abstract

Retrieval is often considered the most important phase in Case-Based Reasoning (CBR), since it lays the foundation for overall performance of CBR systems. Retrieval in CBR aims to retrieve relevant cases that can be successfully used for solving a new problem. To realize retrieval, CBR systems typically rely on a strategy that exploits similarity knowledge, and it is called similarity-based retrieval (SBR). In SBR, similarity knowledge approximates the usefulness of cases for solving a new problem. In this paper, we show that association analysis of stored cases can be used to strengthen SBR. We present a new approach for extracting and representing association knowledge from the cases using association rule mining. We propose a novel retrieval strategy USIM-SCAR that qualitatively enhances SBR by leveraging both similarity and association knowledge. We demonstrate the significant advantages of using USIMSCAR over SBR through an experimental evaluation using medical datasets.