Incremental Sequence Mining
Bhagyshri Lachhwani1, Mehul Barot2, Poonam Sengar3
1Bhagyshri Lachhwani, ME Computer Engineering, LDRP ITR, Gandhinagar. India.
2Mehul Barot, Computer Department ,LDRP ITR, Gandhinagar. India.
3Poonam Sengar, ME Computer Engineering, LDRP ITR, Gandhinagar. India.
Manuscript received on January 23, 2013. | Revised Manuscript received on February 11, 2013. | Manuscript published on February 28, 2013. | PP: 65-69 | Volume-2 Issue-3, February 2013. | Retrieval Number: C0999022313 /2013©BEIESP
Open Access | Ethics and Policies | Cite
© 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: We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. The discovery of frequent sequences in temporal databases is an important data mining problem. Most current work assumes that the database is static, and a database update requires rediscovering all the patterns by scanning the entire old and new database. We consider the problem of the incremental mining of sequential patterns when new transactions or new customers are added to an original database. In this paper, we propose novel techniques for maintaining sequences in the presence of a) database updates, and b) user interaction (e.g. modifying mining parameters). This is a very challenging task, since such updates can invalidate existing sequences or introduce new ones. In both the above scenarios, we avoid re-executing the algorithm on the entire dataset, thereby reducing execution time.
Keywords: Data mining, Frequent sequences, Minimum support, Sequential pattern.