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| Content Provider | ACM Digital Library |
|---|---|
| Author | Verhein, Florian Renz, Matthias Zuefle, Andreas Bernecker, Thomas Kriegel, Hans-Peter |
| Abstract | Probabilistic frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied to standard "certain" transaction databases. The consideration of existential uncertainty of item(sets), indicating the probability that an item(set) occurs in a transaction, makes traditional techniques inapplicable. In this paper, we introduce new probabilistic formulations of frequent itemsets based on possible world semantics. In this probabilistic context, an itemset X is called frequent if the probability that X occurs in at least minSup transactions is above a given threshold τ. To the best of our knowledge, this is the first approach addressing this problem under possible worlds semantics. In consideration of the probabilistic formulations, we present a framework which is able to solve the Probabilistic Frequent Itemset Mining (PFIM) problem efficiently. An extensive experimental evaluation investigates the impact of our proposed techniques and shows that our approach is orders of magnitude faster than straight-forward approaches. |
| Starting Page | 119 |
| Ending Page | 128 |
| Page Count | 10 |
| File Format | PDF MP4 |
| ISBN | 9781605584959 |
| DOI | 10.1145/1557019.1557039 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-06-28 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Probabilistic frequent itemsets Uncertain databases Probabilistic data Frequent itemset mining |
| Content Type | Audio Text |
| Resource Type | Article |
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