Get Advances in Knowledge Discovery and Data Mining: 16th PDF

By Hai-Long Nguyen, Yew-Kwong Woon, Wee-Keong Ng, Li Wan (auth.), Pang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey (eds.)

ISBN-10: 3642302165

ISBN-13: 9783642302169

ISBN-10: 3642302173

ISBN-13: 9783642302176

ISBN-10: 364230219X

ISBN-13: 9783642302190

ISBN-10: 3642302203

ISBN-13: 9783642302206

The two-volume set LNAI 7301 and 7302 constitutes the refereed lawsuits of the sixteenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in may perhaps 2012. the entire of 20 revised complete papers and sixty six revised brief papers have been conscientiously reviewed and chosen from 241 submissions. The papers current new rules, unique learn effects, and useful improvement reports from all KDD-related parts. The papers are geared up in topical sections on supervised studying: energetic, ensemble, rare-class and on-line; unsupervised studying: clustering, probabilistic modeling within the first quantity and on development mining: networks, graphs, time-series and outlier detection, and knowledge manipulation: pre-processing and measurement relief within the moment volume.

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Read or Download Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29 – June 1, 2012, Proceedings, Part II PDF

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Additional info for Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29 – June 1, 2012, Proceedings, Part II

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The Journal of Machine Learning Research 24(2), 123–140 (1996) 4. : Random forests. The Journal of Machine Learning Research 45(1), 5–32 (2001) 5. : Mining high-speed data streams. In: The Sixth ACM SIGKDD, pp. 71–80. ACM (2000) 6. : On the optimality of the simple bayesian classifier under zero-one loss. The Journal of Machine Learning Research 29(2-3), 103–130 (1997) 7. : Multiclass boosting for weak classifiers. The Journal of Machine Learning Research 6, 189–210 (2005) 8. : Experiments with a new boosting algorithm.

It is also possible to detect new types of attacks through an incremental learning process. Additionally, data mining techniques provide the means to easily perform data summarization and visualization, that would be of great help to the security analyst in identifying areas of concern [14]. In the following, we survey the most prominent approaches dedicated to apply data mining techniques within the intrusion detection field. - The MADAM-ID system [10] is considered as the first research work that shows how data mining techniques can be used to construct IDS in a more systematic and automated manner.

The listener adds the most popular label received to its memory. 3) Finally, the post-processing based on the labels in the memories and the threshold r is applied to output the communities. Note that SLPA starts with each node being in its own community (a total of n), the algorithm explores the network and outputs the desired number of communities in the end. As such, the number of communities is not required as an input. Due to the step c, the size of memory increases by one for each node at each step.

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Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29 – June 1, 2012, Proceedings, Part II by Hai-Long Nguyen, Yew-Kwong Woon, Wee-Keong Ng, Li Wan (auth.), Pang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey (eds.)


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