“Frequent Pattern – Projected Sequential Pattern Mining Improve its Efficiency and Scalability”

Authors

  • Ankita M Patel Post Graduate Student of Hasmukh Goswami college of Engineering, Ahmedabad
  • Asst. Prof. Dhaval Patel (Assistant Professor of Computer Engineering Department, Ahmedabad)

Keywords:

Association Rule mining, Data Mining, Frequent Pattern Mining, Parallel Projected database, partition projected database

Abstract

Data mining has become an important field and has been applied extensively across many areas. Mining
frequent item sets in a transaction database is critical for mining association rules. This paper propose a novel
frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial
information about frequent patterns, and develop an efficient FP-tree based mining method. Efficiency of mining is
achieved by the apply parallel projected database and partition projected database in frequent pattern tree to
reduce the database scan, execution time and less memory. So the large database is compressed into a condensed,
smaller data structure. The FP Tree algorithm is faster than the Apriori algorithm and also faster than new proposed
projected database frequent-pattern tree.

Downloads

Published

2016-05-25

How to Cite

Ankita M Patel, & Asst. Prof. Dhaval Patel. (2016). “Frequent Pattern – Projected Sequential Pattern Mining Improve its Efficiency and Scalability”. International Journal of Advance Research in Engineering, Science & Technology, 3(5), 448–455. Retrieved from https://ijarest.org/index.php/ijarest/article/view/655

Issue

Vol. 3 No. 5 (2016): Volume: 03, Issue: 05 - May, 2016.

Section

Articles