Knowledge Discovery Using Heuristics

Knowledge Discovery Using Heuristics

Alina Lazar (Youngstown State University, USA)
DOI: 10.4018/978-1-59140-553-5.ch308
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Abstract

Uninformed or blind search, which processes and evaluates all nodes of a search space in the worst case, is not realistic for extracting knowledge from large data sets because of time constraints that are closely related to the dimension of the data. Generally, the search space increases exponentially with problem size, thereby limiting the size of problems that can realistically be solved using exact techniques such as exhaustive search. An alternative solution is represented by heuristic techniques, which can provide much help in areas where classical search methods failed.

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