An Innovative Approach for finding Frequent Item sets using Maximal Apriori and Fusion Process and Its Evaluation

Finding Frequent Subgraphs in Biological Networks Via Maximal Item Sets

An Efficient Algorithm for Mining Maximal Frequent Item Sets

Problem Statement: In today’s life, the mining of frequent patterns is a basic problem in data mining applications. The algorithms which are used to generate these frequent patterns must perform efficiently. The objective was to propose an effective algorithm which generates frequent patterns in less time. Approach: We proposed an algorithm which was based on hashing technique and combines a ve...

Algorithm for Finding Maximal Frequent Sets

Given a set X and a set C of subsets of X, subsets of X covered by k sets in C are called k-frequent. Frequent sets are of interest in large scale data analysis, pattern recognition and data mining. Characterization of maximal kfrequent sets in terms of equivalence relation and partial order is given. A general algorithm for finding maximal k-frequent sets, efficient for wide range of practical...

Discovery of Maximal Frequent Item Sets using Subset Creation

Data mining is the practice to search large amount of data to discover data patterns. Data mining uses mathematical algorithms to group the data and evaluate the future events. Association rule is a research area in the field of knowledge discovery. Many data mining researchers had improved upon the quality of association rule for business development by incorporating influential factors like u...

A Novel Approach to Mine Frequent Item sets of Process