By Christos Faloutsos (auth.), Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi (eds.)
This booklet constitutes the refereed court cases of the twelfth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2008, held in Osaka, Japan, in may well 2008.
The 37 revised lengthy papers, forty revised complete papers, and 36 revised brief papers offered including 1 keynote speak and four invited lectures have been conscientiously reviewed and chosen from 312 submissions. The papers current new principles, unique learn effects, and useful improvement studies from all KDD-related components together with information mining, facts warehousing, computing device studying, databases, facts, wisdom acquisition, automated medical discovery, info visualization, causal induction, and knowledge-based systems.
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Extra info for Advances in Knowledge Discovery and Data Mining: 12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings
LNCS, vol. 4881, pp. 790–799. Springer, Heidelberg (2007) 4. : The skill plot: a graphical technique for the evaluating the predictive usefulness of continuous diagnostic tests. Biometrics, OnlineEarly Articles (2007) 5. : Wrapper-based computation and evaluation of sampling methods for imbalanced datasets. In: Workshop on Utility-Based Data Mining held in conjunction with the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 179–188 (2005) Cost-Sensitive Classifier Evaluation Using Cost Curves 29 6.
T. set inclusion among those itemsets having the same set of ocurrences in a given databases. • The algorithm MaxMotif (Maximal Motif Miner ) for mining maximal motifs with wildcards . A motif with wildcards  is a sequence of constant symbols in Σ and special wildcards ◦ for single letters such as AB ◦B ◦ABC. • The algorithm CloAtt (Closed Atribute Tree Miner ) for mining maximal trees . An attribute tree  is a labeled unordered tree where out-going edges starting from any internal node mutually distinct symbols in Σ as their labels.
21–24 (2005) 10. : Severe class imbalance: Why better algorithms aren’t the answer. , Torgo, L. ) ECML 2005. LNCS (LNAI), vol. 3720, pp. 539–546. Springer, Heidelberg (2005) 11. : Cost curves: An improved method for visualizing classifier performance. Machine Learning 65(1), 95–130 (2006) 12. : ROC graphs with instance-varying costs. Pattern Recognition Letters 27(8), 882– 891 (2006) 13. : Regret graphs, diagnostic uncertainty, and Youden’s index. Statistics in Medicine 15, 969–986 (1996) 14. : Very simple classification rules perform well on most commonly used datasets.
Advances in Knowledge Discovery and Data Mining: 12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings by Christos Faloutsos (auth.), Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi (eds.)