Advances in Knowledge Discovery and Data Mining: 16th by Hai-Long Nguyen, Yew-Kwong Woon, Wee-Keong Ng, Li Wan

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

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 well 2012. the full of 20 revised complete papers and sixty six revised brief papers have been rigorously reviewed and chosen from 241 submissions. The papers current new rules, unique learn effects, and functional improvement studies from all KDD-related components. The papers are geared up in topical sections on supervised studying: lively, 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 information manipulation: pre-processing and size aid within the moment volume.

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Moreover, current IDS do not provide support for historical data analysis and data summarization [13]. Supporting a historical network database in conjunction with an IDS raises two important technical challenges [8]: (i) since network traffic monitors generate data continuously and at high-rate, the database needs to support a high data insertion rate [8]; (ii) to facilitate the security analysis, the database must quickly answer historical queries [8,13]. Recently, Data Warehouses (DW) and On Line Analytical Processing (OLAP) technologies have gained a widespread acceptance as a support for decision making [7].

Ftp ]T with a class label yt arriving at time t, yi ∈ {y1 , y2 , . . , yc }. A set of l different classifier types, M = {M1 , M2 , . . , Ml }. Output: An heterogenous ensemble E . 1: Initialize the ensemble E with k classifiers of each model in M, denoted as C1 , C2 , . . , Ck∗l . 2: while X has more instance do 3: if chunk is not full then 4: Add xi to chunk. 5: else 6: Perform FCBF to get the relevant and non-redundant feature subset ϕi . 7: if ϕi = ϕi−1 then 8: Find the best accurate classifier Cbest having the smallest aggregated error in the ensemble and get its type.

Firstly, the data is filtered to remove irrelevant information and a relational database is created containing the meaningful remaining data. This database facilities information extraction and data summarization based on individual attributes such as day, source, destination, etc. Secondly, an audit data cube is constructed using the available dimensions. Thirdly, the OMC-IDS system integrates OLAP technology and association rule mining in order to extract interesting information under different perspectives and levels of granularity.

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