Read e-book online Advances in Knowledge Discovery and Data Mining: 17th PDF

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 complaints of the seventeenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. the entire of ninety eight papers provided in those court cases used to be conscientiously reviewed and chosen from 363 submissions. They hide the overall fields of knowledge mining and KDD greatly, together with trend mining, category, graph mining, functions, computer studying, characteristic choice and dimensionality aid, a number of info assets mining, social networks, clustering, textual content mining, textual content class, imbalanced information, privacy-preserving facts mining, suggestion, multimedia information mining, movement facts mining, information 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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An attributed tree, or (atree) is a triple T = (V, E, λ) where (V, E) is the underlying tree and λ : V → D is a function which associates an itemset λ(u) ∈ I to each vertex u ∈ V . The size of an attributed tree is the number of items associated with its vertices. In this paper, we use a string representation for an atree based on that defined for labeled trees by Zaki [24]. This representation is only intended to provide a readable form for atrees. The string representation for an atree T is generated by adding a representation of the nodes found in T in a depth-first preorder traversal of T and adding a special symbol $ when a backtracking from a child to its direct parent occurs.

2(c). Similar to the FP-tree, our PUF-tree maintains horizontal node traversal pointers, which are not shown in the figures for simplicity. Note that we are not confined to sorting and storing items in descending order of expected support. We could use other orderings such as descending order of item caps or of occurrence counts. An interesting observation is that, if we were to store items in descending order of occurrence counts, then the number of nodes in the resulting PUF-tree would be the same as that of the FP-tree.

Communications in Statistics - Theory and Methods 26(6), 1481–1496 (1997) 7. : k-nn as an implementation of situation testing for discrimination discovery and prevention. In: Proceedings of KDD 2011, New York, NY, USA, pp. 502–510 (2011) 8. : An algorithm for multi-relational discovery of subgroups. M. ) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997) PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining of Uncertain Data Carson Kai-Sang Leung and Syed Khairuzzaman Tanbeer Dept.

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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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