Association rule mining is one of the common way to analyze market basket data, and it provides knowledge to the decision-makers that help them to make strategic decisions. In the literature, many techniques have been studied for association rule mining but the exponential growth of data from various sources and the changing nature of data with respect to time and zone makes the analysis task trivial. In this paper, we propose a novel partition-based frequent pattern mining algorithm in order to generate robust and useful patterns from dataset in a more efficient way. The frequent patterns found from the proposed algorithm are used to generate interesting association rules. This paper provides an optimized method, which split the dataset into multiple loads on the basis of a particular attribute depending upon the number of cores available in the system, and these individual loads will get executed in parallel using our proposed algorithm. We show experimental results using datasets from retail sector to validate the capability and usefulness of our proposed algorithm.