Abstract
Efficient algorithms have been developed for mining frequent patterns in traditional data where the content of each transaction is definitely known. It is a core technique used in many mining tasks like sequential pattern mining, correlative mining etc. As we know, fuzzy logic provides a mathematical framework that is compatible with poorly quantitative yet qualitatively significant data. Genetic algorithm (GA) is one of the optimization algorithms, which is invented to mimic some of the processes observed in natural evolution. It is a stochastic search technique based on the mechanism of natural selection and natural genetics. That is a general one, capable of being applied to an extremely wide range of problems. In this paper, we have fuzzified our original dataset and have applied various frequent pattern mining techniques on it. Then the result of a particular frequent pattern mining technique that is frequent pattern (FP) growth is taken into consideration in which we apply the concept of GA. Here, the frequent patterns observed are considered as the set of initial population. For the selection criteria, we consider the mean squared residue score rather using the threshold value. It was observed that out of the three fuzzy based frequent mining techniques and the GA based fuzzy FP growth technique the later finds the best individual frequent patterns. Also, the run time of the algorithm and the number of frequent patterns generated is far better than the rest of the techniques used. To extend our findings we have also compared the results obtained by the GA based fuzzy FP growth with an usual approach on a normalized dataset and then applied the concept of FP growth to find the frequent patterns followed by GA. Then by analyzing the result we found that GA based fuzzy FP growth stills yields the best individual frequent patterns.
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Mishra, D., Mishra, S., Satapathy, S.K., Patnaik, S. (2012). Genetic Algorithm Based Fuzzy Frequent Pattern Mining from Gene Expression Data. In: Patnaik, S., Yang, YM. (eds) Soft Computing Techniques in Vision Science. Studies in Computational Intelligence, vol 395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25507-6_1
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DOI: https://doi.org/10.1007/978-3-642-25507-6_1
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