An Efficient Technique for Mining Sequential Patterns from Standard Data Set |
Author(s): |
| Amit Sariya , MIT, Ujjain; Kshitij Pathak, MIT, Ujjain |
Keywords: |
| Sequential Patterns, Data mining |
Abstract |
|
Mining sequential patterns with time constraints, such as time gaps and sliding time-window, may reinforce the accuracy of mining results. However, the capabilities to mine the time-constrained patterns were previously available only within Apriori framework. Recent studies indicate that pattern-growth methodology could speed up sequence mining. Current algorithms use a generate-candidate-and-test approach that may generate a large amount of candidates for dense datasets. Many candidates do not appear in the database. Therefore we are introducing a more efficient algorithm for sequential pattern mining. The time complexity of proposed algorithm will be lesser in comparison to previous algorithms |
Other Details |
|
Paper ID: IJSRDV2I2424 Published in: Volume : 2, Issue : 2 Publication Date: 01/05/2014 Page(s): 848-850 |
Article Preview |
|
|
|
|
