“Frequent Pattern – Projected Sequential Pattern Mining Improve its Efficiency and Scalability”
Post Graduate Student of Hasmukh Goswami college of Engineering, Ahmedabad, India
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.
