18-06-2013, 12:14 PM
Constraint-based Apriority Algorithm for Mining Long Sequences
Constraint-based Apriority.pptx (Size: 95.53 KB / Downloads: 16)
ABSTRACT
Mining sequential pattern is one of the common data mining
tasks for many real-life applications.
CAMLS(Constraint-based Apriority Algorithm for Mining
Long Sequences) mines the complete set of frequent sequences
(Long) satisfying a min-sup threshold in a sequence.
Mining long sequences will generate an explosive number of
frequent sequences, which is prohibitively costly in both run
time and space storage.
In this paper, we propose to improve CAMLS algorithm to produce only for closed sequences.
Instead of mining full set of sequences, we plan to mine only short(closed) sequences .i.e., those containing, no super sequences with same support.
Our motivation is to mine closed sequences from long sequences using BIDE algorithm with improved CAMLS algorithm and make the pruning strategy even more efficient.
Existing System
Mining sequential patterns is a key objective in the field of data mining due to its wide range of applications.
Given a database of sequences, the challenge is to identify patterns which appear frequently in different sequences.
Well known algorithms such as CAMLS, Constraint-based Apriori Mining of Long Sequences, an efficient algorithm for mining long sequential patterns have proved to be efficient.
Proposed System
In this project we presented an improved CAMLS algorithm to produce only closed sequences and to make the pruning strategy even more efficient.
BIDE is another efficient algorithm which is used to
find closed sequences from long sequences.
So by this we can reduce the runtime and space usage.