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

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed court cases of the 18th Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2014, held in Tainan, Taiwan, in might 2014. The forty complete papers and the 60 brief papers provided inside those complaints have been rigorously reviewed and chosen from 371 submissions. They hide the overall fields of development mining; social community and social media; class; graph and community mining; purposes; privateness retaining; suggestion; characteristic choice and relief; computer studying; temporal and spatial information; novel algorithms; clustering; biomedical facts mining; circulation mining; outlier and anomaly detection; multi-sources mining; and unstructured facts and textual content mining.

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Extra info 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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In the immediate field of interest, anomaly detection, there has been a fair amount of tensor applications. In particular, [2] develop a decomposition model that is suitable for stream data mining and anomaly detection. The authors of [6] introduce a scalable anomaly detection framework using tensors. In [12] the authors perform anomaly detection in a (source IP, destination IP, port number) dataset, and in [7] the authors operate on (source IP, destination IP, port number, timestamp) dataset. Finally, in [3] the authors propose a framework for anomaly detection in multi-aspect data that is based in tensor decompositions.

Both attack groups (A and C) appear only on a single day (January 10th and January 25th respectively). In Figure 1(b) and (c), we are able to discover a new set of anomalies, as we choose a different couple of latent factor vectors to obtain the TensorScore from. In attack group D, we were able to identify an attacker who attempted to trigger 14,652 connections to 236 target Honeynet system IP addresses, with a duration of 8 days. We present the attacks that belong to group E; those attacks focus on a particular Microsoft Network security vulnerability that is associated with ports 139 and 445.

75, 1, and 1 respectively. (iii) Computing the drank of UP The drank of UP is a function of M F , AF and LF . Definition 7. Diverse rank of a frequent pattern Y (drank(Y)): Let Y be the pattern and U be the unbalanced concept hierarchy of height ‘h’. The drank of Y , denoted by drank(Y ), is given by the following equation. l=0 [M F (Y, l, P (Y /E)) ∗ AF (Y, l, P (Y /E))] ∗ LF (l, P (Y /E)) drank(Y, U ) = l=h−1 (5) where, h is the height of the P (P/E), E is the extended unbalanced concept hierarchy, M F (Y, l, P (Y /E)) is the M F of Y at level l, LF (l, P (Y /E)) is the LF at level l and AF (Y, l, P (Y /E)) is the AF of Y at level l.

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Advances in Knowledge Discovery and Data Mining: 18th by Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen,
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