Mining Frequent Item set Using Genetic Algorithm |
Author(s): |
| Prof. Jigar Patel , Alpha College of Engg and technology Khatraj, Ahmadabad 382 481, India; Hardik Patel, Alpha College of Engg and technology Khatraj, Ahmadabad 382 481, India |
Keywords: |
| Genetic Algorithm, time complexity, adaptive heuristic search. |
Abstract |
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By applying rule mining algorithms, frequent itemsets are generated from large data sets e.g. Apriori algorithm. It takes so much computer time to compute all frequent itemsets. We can solve this problem much efficiently by using Genetic Algorithm(GA). GA performs global search and the time complexity is less compared to other algorithms. Genetic Algorithms (GAs) are adaptive heuristic search & optimization method for solving both constrained and unconstrained problems based on the evolutionary ideas of natural selection and genetic. The main aim of this work is to find all the frequent itemsets from given data sets using genetic algorithm & compare the results generated by GA with other algorithms. Population size, number of generation, crossover probability, and mutation probability are the parameters of GA which affect the quality of result and time of calculation. |
Other Details |
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Paper ID: IJSRDV1I3066 Published in: Volume : 1, Issue : 3 Publication Date: 01/06/2013 Page(s): 659-661 |
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