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With the advancement in the web technology it is considered as one of the vast repository of information. However this information is in the hidden form. Various data mining techniques need to be applied for extracting the meaningful information from the web. In this paper the various techniques are discussed that have been used by many researchers for extracting the information and also shown the disadvantages with the existing approaches. The paper put forward a novel concept of mining the association rule from the web data by using Quine-McCluskey algorithm. This algorithm is an optimization technique over the existing algorithm like Apriori, reverse Apriori, k-map. This paper exhibits the working of the QuineMcCluskey algorithm that can extract the frequently accessed web pages with minimum number of candidate sets generation. However the limitation of Quine-McCluskey algorithm is that it cannot find the infrequent patterns.

Quine-McCluskey: A Novel Concept for Mining the Frequency Patterns from Web Data