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 complaints of the sixteenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in could 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 study effects, and sensible improvement reviews from all KDD-related components. The papers are prepared in topical sections on supervised studying: lively, ensemble, rare-class and on-line; unsupervised studying: clustering, probabilistic modeling within the first quantity and on trend mining: networks, graphs, time-series and outlier detection, and information manipulation: pre-processing and size aid within the moment volume.

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Extra 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 latter are classified into “normal” and “intrusion”. OMC-IDS System 15 - The ADAM system [2] is one of the best-known approaches that use association rules mining and classification algorithms to detect intrusions. The main moan that can be addressed to ADAM stands in its high dependency on training data for normal activities. However, the attack-free training data is difficult to afford, since there is no guarantee that we can prevent all attacks in real world networks. - The MINDS system [6] allows the development of scalable data mining algorithms and tools for detecting attacks and threats against computer systems.

Mining time-changing data streams. In: ACM SIGKDD, pp. 97–106. ACM (2001) 13. : Feature selection for high-dimensional data: A fast correlationbased filter solution. In: The 20th ICML, pp. 856–863 (2003) 14. : Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17(4), 491–502 (2005) 15. : Online bagging and boosting. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2340–2345. IEEE (2005) 16.

These are usually numerical values that facilitate a quantitative evaluation of various aspects of interest. Dimensions include attributes that form hierarchies. As long as a hierarchy is traversed from finer to coarser levels, measures are aggregated. Hierarchies can be included in a flat table forming the so-called STAR schema [7]. Fig. 1. A STAR schema for the IDS data warehouse We propose to model the audit data as a multidimensional structure based on the STAR schema shown in Figure 1. The fact table “Connections” contains the attribute “#Connection” that measures the number of connections.

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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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