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http://hdl.handle.net/1942/13632
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DC Field | Value | Language |
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dc.contributor.author | Calders, Toon | - |
dc.contributor.author | Dexters, Nele | - |
dc.contributor.author | GILLIS, Joris | - |
dc.contributor.author | GOETHALS, Bart | - |
dc.date.accessioned | 2012-05-02T09:46:22Z | - |
dc.date.available | 2012-05-02T09:46:22Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | INFORMATION SYSTEMS, 39, p. 233-255 | - |
dc.identifier.issn | 0306-4379 | - |
dc.identifier.uri | http://hdl.handle.net/1942/13632 | - |
dc.description.abstract | Mining frequent itemsets in a datastream proves to be a difficult problem, as itemsets arrive in rapid succession and storing parts of the stream is typically impossible. Nonetheless, it has many useful applications; e.g., opinion and sentiment analysis from social networks. Current stream mining algorithms are based on approximations. In earlier work, mining frequent items in a stream under the max-frequency measure proved to be effective for items. In this paper, we extended our work from items to itemsets. Firstly, an optimized incremental algorithm for mining frequent itemsets in a stream is presented. The algorithm maintains a very compact summary of the stream for selected itemsets. Secondly, we show that further compacting the summary is non-trivial. Thirdly, we establish a connection between the size of a summary and results from number theory. Fourthly, we report results of extensive experimentation, both of synthetic and real-world datasets, showing the efficiency of the algorithm both in terms of time and space. | - |
dc.description.sponsorship | FWO | - |
dc.language.iso | en | - |
dc.subject.other | Frequent itemset mining; Datastream; Theory; Algorithm; Experiments | - |
dc.title | Mining frequent itemsets in a stream | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 255 | - |
dc.identifier.spage | 233 | - |
dc.identifier.volume | 23 | - |
local.format.pages | 23 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Gillis, JJM (reprint author),Hasselt Univ, Agoralaan Gebouw D, B-3590 Diepenbeek, Belgium, joris.gillis@uhasselt.be | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1016/j.is.2012.01.005 | - |
dc.identifier.isi | 000329531300012 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
item.contributor | Calders, Toon | - |
item.contributor | Dexters, Nele | - |
item.contributor | GILLIS, Joris | - |
item.contributor | GOETHALS, Bart | - |
item.validation | ecoom 2015 | - |
item.fullcitation | Calders, Toon; Dexters, Nele; GILLIS, Joris & GOETHALS, Bart (2014) Mining frequent itemsets in a stream. In: INFORMATION SYSTEMS, 39, p. 233-255. | - |
crisitem.journal.issn | 0306-4379 | - |
crisitem.journal.eissn | 1873-6076 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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paper.pdf | Peer-reviewed author version | 420.41 kB | Adobe PDF | View/Open |
calders 1.pdf | Published version | 1.61 MB | Adobe PDF | View/Open |
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