Comparative Analysis of Top-Down & Bottom-up Approach (Apriori Algorithm)

Tremendous amount of data being collected is increasing speedily by computerized applications around the world. Hidden in the vast data, the valuable information is attracting researchers of multiple disciplines to study effective approaches to derive useful knowledge from within. Among various data mining objectives, the mining of frequent patterns has been the focus of knowledge discovery in databases. This thesis aims to investigate efficient algorithm for mining including association rules and sequential patterns. Many algorithms have been proposed from last many decades including horizontal layout based techniques, vertical layout based techniques, and projected layout based techniques. But most of the techniques suffer from repeated database scan, Candidate generation (Apriori Algorithms), memory consumption problem and many more for mining frequent patterns. As in retailer industry many transactional databases contain same set of transactions many times, to apply this thought, in this thesis present an improved Apriori algorithm that guarantee the better performance than classical Apriori algorithm.