An Overview of Heuristic Knowledge Discovery for Large Data Sets Using Genetic Algorithms and Rough Sets

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 close related to the dimension of the data. Generally, the search space increases exponentially with problem size thereby limiting the size of problems which 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. The word "heuristic" comes from Greek and means "to know", "to find", "to discover" or "to guide an investigation". Specifically, "Heuristics are techniques which seek good (near-optimal) solutions at a reasonable computational cost without being able to guarantee either feasibility or optimality, or even in many cases to state how close to optimality a particular feasible solution is." (Russell, Norvig, 1995) Heuristic refers to any techniques that improves the average-case performance on a problem-solving task but does not necessarily improve the worst case performance. Heuristic techniques search the problem space "intelligently" using knowledge of previously tried solutions to guide the search into fruitful areas of the search space. Often, search spaces are so large that only heuristic search can produce a solution in reasonable time. These techniques improve the efficiency of a search process, sometimes by sacrificing the completeness or the optimality of the solution. Heuristics are estimates of the distance remaining to the goal, estimates computed based on the domain knowledge. The domain knowledge provides help to heuristics in guiding the search and can be represented in a variety of knowledge formats. These formats include patterns, networks, trees, graphs, version spaces, rule sets, equations, and contingency tables. With regard to heuristics there are a number of generic approaches such as greedy, A* search, tabu search, simulating annealing, and population-based heuristics. The heuristic methods can be applied to a wide class of problems in optimization, classification, statistics, recognition, planning and design. Of special interest is the integration of heuristic search principles with the dynamic processes in which data becomes available in successive stages, or where