Incremental-Eclat Model

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dc.contributor.authorWan Aezwani Bt Wan Abu Bakar
dc.contributor.authorZailani B. Abdullah
dc.contributor.authorMd. Yazid B. Md Saman
dc.contributor.authorMasita@Masila Bt Abd Jalil
dc.contributor.authorMustafa B. Man
dc.contributor.authorTutut Herawan
dc.contributor.authorAbdul Razak Hamdan
dc.date.accessioned2017-04-11T03:26:41Z
dc.date.available2017-04-11T03:26:41Z
dc.date.issued2016
dc.description.abstractAssociation Rule Mining (ARM) is one of the most prominent areas in detecting pattern analysis especially for crucial business decision making. With the aims to extract interesting correlations, frequent patterns, association or casual structures among set of items in the transaction databases or other data repositories, the end product of association rule mining is the analysis of pattern that could be a major contributor especially in managerial decision making. Most of previous frequent mining techniques are dealing with horizontal format of their data repositories. However, the current and emerging trend exists where some of the research works are focusing on dealing with vertical data format and the rule mining results are quite promising. One example of vertical rule mining technique is called Eclat which is the abbreviation of Equivalence Class Transformation.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/5672
dc.language.isoenen_US
dc.publisherLecture Notes in Electrical Engineeringen_US
dc.subjectAssociation rule miningen_US
dc.subjectRelational databaseen_US
dc.subjectMysqlen_US
dc.subjectFrequent itemseten_US
dc.subjectEclat algorithmen_US
dc.titleIncremental-Eclat Modelen_US
dc.title.alternativeAn Implementation via Benchmark Case Studyen_US
dc.typeArticleen_US
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