Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/343171
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dc.coverage.spatialImproving the efficiency inDiscovering high utility itemsetsUsing parallel processing methods
dc.date.accessioned2021-10-05T11:22:56Z-
dc.date.available2021-10-05T11:22:56Z-
dc.identifier.urihttp://hdl.handle.net/10603/343171-
dc.description.abstractFrequent Itemset Mining (FIM) is a noteworthy subdivision of datamining. FIM identifies frequent patterns by considering the number ofoccurrences of particular items in the transactions of a transaction database.An itemset is deliberated as frequent only if its support count fulfils theminimum support threshold value that the user decides. The FIM algorithmstudies only the number of occurrences of items in the transaction. Therenowned algorithms for frequent itemsets mining are Apriori, FrequentPattern (FP) growth, ECLAT etc. Later, several variants of FIM have beenaddressed such as periodic frequent itemset mining, infrequent itemsetmining, time series based periodic frequent itemset mining etc. newlinePeriodic Frequent Itemset Mining (PFIM) uncovers itemset thatoccur periodically in the transactions. The term Periodicity means thetendency to occur at regular intervals. The occurrence may be bounded withannual occurrence, seasonal occurrence, transaction intervals based ontransaction number alone without any time bounds etc. The need to associateperiodicity in mining is to find the period of frequent item which may help to newlineenhance the business by increasing the stock of frequent items in thatparticular time period. Several works have been carried out to find periodicpatterns in the transaction database that even borrows idea from the FIMworks existing in the literature.Infrequent Itemset Mining (IIM) is a concept that discovers rarecase of itemsets. In contrast with FIM, infrequent itemsets are those whosefrequency falls below minimum threshold value supplied by the user. Eventhough these itemsets are infrequent in occurrence but has proven its potential newlinein the field of bio-informatics newline newline
dc.format.extentxvi,127p
dc.languageEnglish
dc.relationp.117-126
dc.rightsuniversity
dc.titleImproving the efficiency inDiscovering high utility itemsetsUsing parallel processing methods
dc.title.alternative
dc.creator.researcherArunkumarM S
dc.subject.keyword
dc.subject.keywordhigh utility itemsets
dc.subject.keywordparallel processing methods
dc.description.note
dc.contributor.guideSuresh P and Gunavathi C
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registeredn.d.
dc.date.completed2020
dc.date.awarded2020
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf137.79 kBAdobe PDFView/Open
03_vivaproceedings.pdf244.11 kBAdobe PDFView/Open
04_bonafidecertificate.pdf196.2 kBAdobe PDFView/Open
05_abstracts.pdf902.94 kBAdobe PDFView/Open
06_acknowledgements.pdf254.07 kBAdobe PDFView/Open
07_contents.pdf684.01 kBAdobe PDFView/Open
08_listoftables.pdf894.58 kBAdobe PDFView/Open
09_listoffigures.pdf661.24 kBAdobe PDFView/Open
10_listofabbreviations.pdf789.46 kBAdobe PDFView/Open
11_chapter1.pdf850.86 kBAdobe PDFView/Open
12_chapter2.pdf901.73 kBAdobe PDFView/Open
13_chapter3.pdf730.13 kBAdobe PDFView/Open
14_chapter4.pdf870.95 kBAdobe PDFView/Open
15_chapter5.pdf731.29 kBAdobe PDFView/Open
16_chapter6.pdf683.27 kBAdobe PDFView/Open
17_conclusion.pdf810.16 kBAdobe PDFView/Open
18_references.pdf845.37 kBAdobe PDFView/Open
19_listofpublications.pdf810.74 kBAdobe PDFView/Open
80_recommendation.pdf59.93 kBAdobe PDFView/Open


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