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dc.contributor.authorKoh, YSen_NZ
dc.contributor.authorPears, RLen_NZ
dc.contributor.editorMadria, SKen_NZ
dc.contributor.editorHara, Ten_NZ
dc.date.accessioned2016-01-19T23:51:33Z
dc.date.available2016-01-19T23:51:33Z
dc.date.copyright2015-09-01en_NZ
dc.identifier.citationIn: Madria S., Hara T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science, vol 9263. Springer, Chamen_NZ
dc.identifier.issn1611-3349en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/9370
dc.description.abstractWe propose an efficient algorithm, called HI-Tree, for mining high influence patterns for an incremental dataset. In traditional pattern mining, one would find the complete set of patterns and then apply a post-pruning step to it. The size of the complete mining results is typically prohibitively large, despite the fact that only a small percentage of high utility patterns are interesting. Thus it is inefficient to wait for the mining algorithm to complete and then apply feature selection to post-process the large number of resulting patterns. Instead of generating the complete set of frequent patterns we are able to directly mine patterns with high utility values in an incremental manner. In this paper we propose a novel utility measure called an influence factor using the concepts of external utility and internal utility of an item. The influence factor for an item takes into consideration its connectivity with its neighborhood as well as its importance within a transaction. The measure is especially useful in problem domains utilizing network or interaction characteristics amongst items such as in a social network or web click-stream data. We compared our technique against state of the art incremental mining techniques and show that our technique has better rule generation and runtime performance.
dc.publisherSpringer
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation).
dc.subjectPrefix-tree; High influence patterns; FP-growth
dc.titleHI-Tree: Mining High Influence Patterns Using External and Internal Utility Valuesen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1007/978-3-319-22729-0_4
pubs.elements-id183408


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