Using Hyper-structure mining to Ascertain Recurrent Patterns in Large Dataset

Abstract

Data mining (DM) is the process of exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules.One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. Frequent itemsets play an important role in many data mining tasks. The task of finding the frequent itemsets is a fundamental problem in data mining. In this paper we examine the problem of finding frequent itemsets using Hyper-structure mining of frequent patterns in large databases (H-Mine) algorithm on Mushroom Dataset. The performance of the algorithm is evaluated with respect to execution time and frequent pattern generation. The Mushroom dataset contains characteristics of various species of mushrooms, and was originally obtained from the UCI Repository of Machine Learning Databases. H-Mine is one of the efficient algorithm for mining frequent itemsets in a set of transactions. H-mine uses the advantage of the H-struct data structure and dynamically adjusts links in the mining process. A distinct feature of this method is that it has very limited and precisely predictable space overhead and runs really fast in memory-based setting. . In this paper we describe the implementation of this algorithm with respect to both execution time and frequent pattern generation.