New PDF release: Advances in Knowledge Discovery and Data Mining: 17th

By Rob M. Konijn, Wouter Duivesteijn (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)

ISBN-10: 3642374522

ISBN-13: 9783642374524

ISBN-10: 3642374530

ISBN-13: 9783642374531

The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed lawsuits of the seventeenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. the complete of ninety eight papers offered in those court cases used to be conscientiously reviewed and chosen from 363 submissions. They hide the overall fields of knowledge mining and KDD widely, together with trend mining, class, graph mining, functions, computing device studying, characteristic choice and dimensionality relief, a number of info assets mining, social networks, clustering, textual content mining, textual content category, imbalanced information, privacy-preserving information mining, suggestion, multimedia information mining, flow info mining, info preprocessing and representation.

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Extra info for Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I

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Corollary 1. Let (i) X be a k-itemset (where k > 1) with expSupCap (X) ≥ minsup in the DB and (ii) Y be an itemset in the X-projected DB (denoted as DBX ). Then, expSupCap (Y ∪ X) in the DB ≥ minsup if and only if expSupCap (Y ) in all the transactions in DBX ≥ minsup. Proof. , Y ∈ DBX ). PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining 21 Fig. 3. Our PUF-growth mines frequent patterns from the PUF-tree in Fig. , X) as itemset Y in DBX . Moreover, due to the definition of projected DBs, the transactions that contain (Y ∪ X) in the DB are identical to those transactions that contain Y in DBX .

A tree S = (V, E) is a directed, acyclic and connected graph where V is a set of vertices (nodes) and E = {(u, v)|u, v ∈ V } is a set of edges. A distinguished node r ∈ V is considered as the root, and for any other node x ∈ V , there is a unique path from r to x. If there is a path from a vertex u to v in S = (V, E), then u is an ancestor of v (v is a descendant of u). e. u is an immediate ancestor of v), then u is the parent of v (v is a child of u). An ordered tree has a left-to-right ordering among the siblings.

So, we also compared our PUF-growth with UH-Mine. Figs. 4(a)–(c) show that PUF-growth took shorter runtime than UH-Mine for datasets u100k10L 50 60, u100k10L 10 100 and mushroom 50 60. The primary reason is that, even though the UH-Mine finds the exact set of frequent patterns when mining an extension of X, it may suffer from the high computation cost of calculating the expected support of X on-the-fly for all transactions containing X. , in u100k10L 50 60, u100k10L 10 100, retail 50 60). 3 Number of False Positives In practice, although both UFP-tree and PUF-trees are compact, their corresponding algorithms generate some false positives.

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Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I by Rob M. Konijn, Wouter Duivesteijn (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)


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