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Title:
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MINING FREQUENT CLOSED ITEMSETS FROM PRIVACY PRESERVING DATA |
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Author(s):
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Kauzyo Narita , Horoyuki Kitagawa |
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ISBN:
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972-8939-03-5 |
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Editors:
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Pedro Isaías, Piet Kommers and Maggie McPherson |
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Year:
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2005 |
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Edition:
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Single |
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Keywords:
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Association rules, Closed itemsets, Privacy preservation, Randomization. |
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Type:
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Full Paper |
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First Page:
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355 |
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Last Page:
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362 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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As we face huge amounts of varied information, data mining, which helps us discover features or rules from voluminous data, has become more important. In association rules mining, the mining of frequent closed itemsets is attracting a lot of attention; here, only frequent itemsets having no proper superset [5, 4] are mined. Beyond that, privacy preserving mining has become a hot research issue because of increased interest in privacy [1, 7, 8]. This paper proposes a novel algorithm to mine frequent closed itemsets under the constraints of privacy preservation. To the best of our knowledge, there are no proposals on mining frequent closed itemsets that take privacy preservation into account. The proposed algorithm is based on J. Hans FP-tree [3, 4]. |
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