Show simple item record

dc.contributor.authorMohd Shaharanee, Izwan
dc.contributor.authorHadzic, Fedja
dc.contributor.authorDillon, Tharam S.
dc.contributor.editorByoung Tak Zhang and Mehmet A Orgun
dc.date.accessioned2017-01-30T12:06:26Z
dc.date.available2017-01-30T12:06:26Z
dc.date.created2011-03-21T20:01:27Z
dc.date.issued2010
dc.identifier.citationMohd Shaharanee, Izwan Nizal and Hadzic, Fedja and Dillon, Tharam S. 2010. A Statistical Interestingness Measures for XML based Association Rules, in Zhang, B.T. and Orgun, M.A. (ed), Lecture Notes in Computer Science, Volume 6230: Trends in Artificial Intelligence (PRICAI 2010). pp. 194-205. Germany: Springer.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/18190
dc.description.abstract

Recently mining frequent substructures from XML data has gained a considerable amount of interest. Different methods have been proposed and examined for mining frequent patterns from XML documents efficiently and effectively. While many frequent XML patterns generated are useful and interesting, it is common that a large portion of them is not considered as interesting or significant for the application at hand. In this paper, we present a systematic approach to ascertain whether the discovered XML patterns are significant and not just coincidental associations, and provide a precise statistical approach to support this framework. The proposed strategy combines data mining and statistical measurement techniques to discard the non significant patterns. In this paper we considered the “Prions” database that describes the protein instances stored for Human Prions Protein. The proposed unified framework is applied on this dataset to demonstrate its effectiveness in assessing interestingness of discovered XML patterns by statistical means. When the dataset is used for classification/prediction purposes, the proposed approach will discard non significant XML patterns, without the cost of a reduction in the accuracy of the pattern set as a whole.

dc.publisherSpringer
dc.subjectdata mining
dc.subjectsemi-structured data
dc.subjectstatistical analysis
dc.subjectinteresting rules
dc.titleA Statistical Interestingness Measures for XML based Association Rules
dc.typeBook Chapter
dcterms.source.startPage194
dcterms.source.endPage205
dcterms.source.titleLecture notes in computer science, volume 6230: trends in artificial intelligence (PRICAI 2010)
dcterms.source.isbn9783642152450
dcterms.source.placeHeidelberg
dcterms.source.chapter73
curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusFulltext not available


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record