| dc.contributor.author |
Naik, S.B. |
|
| dc.contributor.author |
Pawar, J.D. |
|
| dc.date.accessioned |
2015-06-04T03:55:35Z |
|
| dc.date.available |
2015-06-04T03:55:35Z |
|
| dc.date.issued |
2013 |
|
| dc.identifier.citation |
19. Int. Conf. on Management of Data (COMAD). 2013; 117-120. |
en_US |
| dc.identifier.uri |
http://irgu.unigoa.ac.in/drs/handle/unigoa/2907 |
|
| dc.identifier.uri |
http://dl.acm.org/citation.cfm?id=2694503 |
|
| dc.description.abstract |
The purpose of this work is to mine closed frequent itemsets from transactional data streams using a sliding window model. An efficient algorithm IMCFI is proposed for <u>I</u>ncremental <u>M</u>ining of <u>C</u>losed <u>F</u>requent Itemsets from a transactional data stream. The proposed algorithm IMCFI uses a data structure called INdexed Tree(INT) similar to NewCET used in NewMoment[5]. INT contains an index table ItemSet Reference List (ISRList) which contains information about the locations of closed frequent itemsets stored in the summary data structure. ISRList helps in quick searching of closed frequent itemset already generated which in turn reduces the overall time required to mine new closed frequent itemsets from a sliding window. Experiments show that IMCFI is time and space efficient when compared to NewMoment. |
|
| dc.subject |
Computer Science and Technology |
en_US |
| dc.title |
An Efficient Incremental Algorithm to Mine Closed Frequent Itemsets over Data Streams |
en_US |
| dc.type |
Conference article |
en_US |