Presentation 2006-07-14
Association Rule Mining for a Generalized Noisy Data Model
Kazuyo NARITA, Hiroyuki KITAGAWA,
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Abstract(in English) As we face huge amounts of varied information, data mining, which helps us discover hidden features or rules from voluminous data systematically, has become more important. However, many data in real world are dirty, including noises such as missing values or irrelevant values. The information mined from such noisy data becomes incorrect. In our previous work [1], we assumed a noisy data model which involves two kinds of noise: one is that an item which should be in a transaction erronously disappears, and another that an item which should not be in a transaction erronously appears. We proposed the method to estimate frequent itemsets [2] on the noiseless data, by probabilistic calculation using the noisy one. However, the real world data may include more complex patterns of noises. In this paper, we present a more generalized noisy data model, and discuss association rule mining under the model.
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Keyword(in English) data mining / association rule mining / knowledge discovery / noisy data model
Paper # DE2006-100
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Committee DE
Conference Date 2006/7/7(1days)
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Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Association Rule Mining for a Generalized Noisy Data Model
Sub Title (in English)
Keyword(1) data mining
Keyword(2) association rule mining
Keyword(3) knowledge discovery
Keyword(4) noisy data model
1st Author's Name Kazuyo NARITA
1st Author's Affiliation Graduate School of Systems and Information Engineering, University of Tsukuba()
2nd Author's Name Hiroyuki KITAGAWA
2nd Author's Affiliation Graduate School of Systems and Information Engineering, University of Tsukuba
Date 2006-07-14
Paper # DE2006-100
Volume (vol) vol.106
Number (no) 150
Page pp.pp.-
#Pages 6
Date of Issue