Read e-book online Advances in Knowledge Discovery and Data Mining: 8th PDF

By Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang

ISBN-10: 354022064X

ISBN-13: 9783540220640

This publication constitutes the refereed lawsuits of the eighth Pacific-Asia convention on wisdom Discovery and knowledge mining, PAKDD 2004, beld in Sydney, Australia in may possibly 2004.

The 50 revised complete papers and 31 revised brief papers offered have been rigorously reviewed and chosen from a complete of 238 submissions. The papers are geared up in topical sections on class; clustering; organization ideas; novel algorithms; occasion mining, anomaly detection, and intrusion detection; ensemble studying; Bayesian community and graph mining; textual content mining; multimedia mining; textual content mining and net mining; statistical tools, sequential info mining, and time sequence mining; and biomedical info mining.

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Read Online or Download Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings PDF

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Additional resources for Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings

Example text

In addition, the dense cells will cover most points of the data sets, and the use of density-connected relationship is helpful to classify and to identify the main body of each cluster. The remaining work is hence to discover the boundaries of clusters. Definition 5. A cell is called isolated if its neighboring cells are all cells. The data points, which are contained in some isolated sparse cells, are defined as noises. A proper density-connected equivalent subclass usually contains most data points of each cluster.

Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation, 10(7) 1895–1924, 1998 6. C. Nadeau and Y. Bengio. Inference for the generalization error. In Machine Learning 52:239– 281, 2003 7. R. Quinlan. 5: Programs for Machine Learning, Morgan Kaufmann, 1993. 8. N. Friedman, D. Geiger, and M. Goldszmidt. Bayesian Network Classifiers. In Machine Learning 29:131–163, 1997 9. J. Pearl: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1988.

This is consistent with the results observed on the UCI datasets. Table 5 shows that the tests have fewer problems with data sources 1 and 4 (apart from the 5 × 2 cv test), where it is easy to decide whether the two schemes differ. The 5 × 2 test has problems with data source 4 because it is a rather conservative test (low Type I error, high Type II error) and tends to err on the side of being too cautious when deciding whether two schemes differ. 5 Conclusions We considered tests for choosing between two learning algorithms for classification tasks.

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Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings by Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang


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