Date of Publication :2nd January 2018

Abstract: Pattern mining algorithms are used to mine the useful data from the massive amount of IOT data. Mostly used data mining algorithms are classification, clustering, association rule and regression in which the classification and regression come under supervised learning and other two in unsupervised learning. The objective is to review different techniques applied for mining the pattern by using classification, clustering, and other algorithms. By applying parallel data mining algorithm in map-reduce framework. We have also implemented the algorithm for mining the frequent pattern from the datasets. Apriori and FP Growth algorithm have been implemented practically on the market basket dataset. As per the result, we have concluded that Apriori is better than FP Growth in terms of the time stamp. And FP Growth is better than Apriori in terms of large datasets. This paper represents the problems occur in this type of methods with little bit solution of them by a new modified algorithm.

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