A Compressed Tree-based Approach for Frequent Item Sets Mining from Uncertain Data

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Provided by: International Association of Scientific Innovation and Research (IASIR)
Topic: Data Management
Format: PDF
Over the past decade, there have been many studies on mining frequent item sets from precise data in which the presence and absence of items in transactions was certainly known. In some applications, the presence and absence of items in transactions are uncertain. The existential probabilities of these items are ranging from 0 to 1.To deal with these situations, a compressed tree based mining algorithm to find frequent patterns from imprecise data is proposed. UF-Tree (Uncertain Frequent Pattern Tree) method was introduced to construct the UF-Tree structure for mining frequent patterns. In UF-Tree structure, the same items with different probabilities are not merged together, thus causing many repeated nodes in the tree.
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