Sequential Pattern Mining on Library Transaction Data

Please use this identifier to cite or link to this item: http://repository.ipb.ac.id/handle/123456789/81043

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DC Field	Value	Language
dc.contributor.author	Sitanggang, Imas Sukaesih	-
dc.contributor.author	Husin, Nor Azura	-
dc.contributor.author	Agustina, Anita	-
dc.contributor.author	Mahmoodian, Naghmeh	-
dc.date.accessioned	2016-06-13T02:57:12Z	-
dc.date.available	2016-06-13T02:57:12Z	-
dc.date.issued	2010	-
dc.identifier.isbn	978-1-4244-6716-7	-
dc.identifier.uri	http://repository.ipb.ac.id/handle/123456789/81043	-
dc.description.abstract	Application of data mining techniques in library data results interesting and useful patterns that can be used to improve services in university libraries. This paper presents results of the work in applying the sequential pattern mining algorithm namely AprioriAll on a library transaction dataset. Frequent sequential patterns containing book sequences borrowed by students are generated for minimum supports 0.3, 0.2, 0.15 and 0.1. These patterns can help library in providing book recommendation to students, conducting book procurement based on readers need, as well as managing books layout.	id
dc.language.iso	en	id
dc.publisher	Universiti Teknologi PETRONAS	id
dc.title	Sequential Pattern Mining on Library Transaction Data	id
dc.type	Article	id
dc.subject.keyword	Sequential Pattern Mining	id
dc.subject.keyword	AprioriAll	id
dc.subject.keyword	Library Transaction Data	id
Appears in Collections:	Computer Science

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