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A Comparative Study on Frequent Item Set Generation Algorithms

M. Nirmala, Dr.V. Palanisamy


The most significant tasks in data mining are the process of discovering frequent item sets and association rules. Numerous efficient algorithms are available in the literature for mining frequent item sets and association rules. The time required for generating frequent item sets plays an important role. Some algorithms are designed, considering only the time factor. Incorporating utility considerations in data mining tasks is gaining popularity in recent years. Our study includes depth analysis of algorithms and discusses some problems of generating frequent item sets from the algorithm. The time of execution for each data set is also well analyzed. The work yields a detailed analysis of the algorithms to elucidate the performance with standard dataset like Adult, Mushroom etc. The comparative study of algorithms includes aspects like different support values, size of transactions and different datasets.


Data Mining, FP Growth, Frequent Item Set Mining, Mushroom

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