Abstract
Knowledge acquisition is considered as an extraordinary issue concerning organizations and decision makers nowadays. Learning from previous failures and successes saves plenty of time in understanding the problems and visualizing data. Case-based Reasoning (CBR) as a process is one of the most used methods to solve the problem of knowledge capture and data understanding. In this paper we proposed an approach for clustering theses documents based on CBR combined with lexical similarity and k-means algorithm for cluster-dependent keyword weighting. The cluster dependent keyword weighting help in partitioning and categorizing the theses documents into more meaningful categories. The proposed approach yield to 91.95 % increase of using CBR in comparison to human assessments.
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Ayeldeen, H., Hegazy, O., Hassanien, A.E. (2015). Case Selection Strategy Based on K-Means Clustering. In: Mandal, J., Satapathy, S., Kumar Sanyal, M., Sarkar, P., Mukhopadhyay, A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 339. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2250-7_39
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DOI: https://doi.org/10.1007/978-81-322-2250-7_39
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