抽象的

A Study on the Performance of CT-APRIORI and CT-PRO Algorithms using Compressed Structures for Pattern Mining

Mrs. A.B. Dhivya and Dr. (Mrs.) B.Kalpana

Many algorithms have been proposed to improve the performance of mining frequent patterns from transaction databases. Pattern growth algorithms like FP-Growth based on the FP-tree are more efficient than candidate generation and test algorithms. In this paper, we propose a new data structure named Compressed FP-Tree (CFP-Tree) and an algorithm named CT-PRO that performs better than the current algorithms including FP-Growth and Apriori. The number of nodes in a CFP-Tree can be up to 50% less than in the corresponding FP-Tree. CT-PRO is empirically compared with FP-Growth and Apriori. CT-PRO is also extended for mining very large databases and its scalability evaluated experimentally.All these results point CT-PRO as the right candidate for generating a compact version of the original transaction database, which is small in size and which performs frequent pattern mining in a fast and efficient manner