An Efficient and Effective Method for Sequential Rule Mining
Vinay Raj Pandey1, Shivesh Tiwari2, Arun Kumar Shukla3, Ashutosh Shukla4
1Vinay Raj Pandey, Department of CS & IT, SHIATS, Allahabad (Uttar Pradesh), India.
2Shivesh Tiwari, Department of CS & IT, BBSCET, Allahabad (Uttar Pradesh), India.
3Arun Kumar Shukla, Department of CS & IT, SHIATS, Allahabad (Uttar Pradesh), India.
4Ashutosh Shukla, Department of Computer Science & Engineering, BIT Mesra, Ranchi (Jharkhand), India.
Manuscript received on 15 June 2015 | Revised Manuscript received on 25 June 2015 | Manuscript Published on 30 June 2015 | PP: 5-7 | Volume-4 Issue-5, June 2015 | Retrieval Number: E3991064515/15©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Tremendous amount of data being collected is increasing speedily by computerized applications around the world. Hidden in the vast data, the valuable information is attracting researchers of multiple disciplines to study effective approaches to derive useful knowledge from within. This thesis aims to investigate efficient algorithm for mining including association rules and sequential patterns. 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 rule mining. The time & space consumption of proposed algorithm will be lesser in comparison to previous algorithms.
Keywords: Sequential rule Mining, Confidence, Support
Scope of the Article: Data Mining