New PDF release: Advances in Knowledge Discovery and Data Mining: 18th

By Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao

ISBN-10: 3319066072

ISBN-13: 9783319066073

ISBN-10: 3319066080

ISBN-13: 9783319066080

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed lawsuits of the 18th Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2014, held in Tainan, Taiwan, in may possibly 2014. The forty complete papers and the 60 brief papers awarded inside those lawsuits have been conscientiously reviewed and chosen from 371 submissions. They disguise the overall fields of trend mining; social community and social media; type; graph and community mining; purposes; privateness retaining; suggestion; function choice and aid; desktop studying; temporal and spatial info; novel algorithms; clustering; biomedical info mining; circulation mining; outlier and anomaly detection; multi-sources mining; and unstructured facts and textual content mining.

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Additional resources for Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I

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Extracting Diverse Patterns with Unbalanced Concept Hierarchy 19 Definition 1. Balanced Pattern (BP): Consider a pattern Y = {i1 , i2 , · · · , in } with ‘n’ items and a concept hierarchy of height ‘h’. The pattern Y is called balanced pattern, if the height of all the items in Y is equal to ‘h’. Definition 2. Projection of Balanced Concept Hierarchy for Y (P (Y /C)): Let Y be BP and C be balanced concept hierarchy. The P (Y /C) is the projection of C for Y which contains the portion of C. All the nodes and edges exists in the paths of the items of Y to the root, along with the items and the root, are included in P (Y /C).

H. Mao et al. (a) (b) (c) Fig. 1. Scatter plot in Honeynet result from both attackers and victims views. (a) In 1st -2nd concept of source IP view, we observe three different cases, case A is POP3 (port 110) brute force attacks, and case C is port scanning in port 25. Case B contains a lot of instances but cannot be separated in this plot ; (b) In 2nd 3rd concept sof source IP view, case C and D appear in this plot which is medium scale of scanning behavior, using ports 22, 23, 135 and 445; (c) In 4th -5th concepts of source IP view, case E appears which represents another scanning behavior.

To that end, we have two choices, in order to alleviate this issue: We may, either, make our data binary, where the tensor, we may take the logarithm of the counts, so that we compress very big values. Tensor Formulation of Our Problem In order to form a tensor out of the data that we posses, we create a tensor entry for each (i, j, k) triple of, say (source IP, target IP, timestamp) that exists in our data log. The choice for the value for each (i, j, k) varies: we can have the raw counts of connections, we can compress that value (by taking its logarithm), or we can simply indicate that such a triplet exists in our log, by setting that value to 1.

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Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I by Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao


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