A Survey on Clustering Approaches for Textual Case Based Reasoning
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التاريخ
المؤلفين
عنوان الدورية
ردمد الدورية
عنوان المجلد
الناشر
The Fifty-one international conference for Statistics, Computer Science and Operation Research
خلاصة
Case Based Reasoning (CBR) solves problems using the already stored knowledge, captures new knowledge, and making it immediately available for solving the next problem. Therefore, CBR can be seen as a method to capture new experience and make it immediately available for problem solving. It can be seen also as an incremental learning and knowledge-discovery approach, since it can capture from new experience general knowledge. CBR that can manipulate with unstructured data like texts and documents is called Textual CBR. One of the key issues in Case-Based Reasoning (CBR) systems is the efficient retrieval of cases when the case base is huge and/or it contains uncertainty and partial knowledge. This can be solved by using clustering techniques. Clustering is featured by grouping cases according to their similarities and represents each one of these groups by prototypes. Thus, the retrieve phase carries out a selective retrieval that is focused on using only the subset of cases potentially similar to the new case to solve. A survey on the hard, the soft clustering approaches and some of the applied optimizations is carried out in this paper to show the efficiency and applicability of text clustering and how optimizations can refine the results.