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

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 lawsuits of the 18th Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2014, held in Tainan, Taiwan, in may perhaps 2014. The forty complete papers and the 60 brief papers provided inside of those lawsuits have been conscientiously reviewed and chosen from 371 submissions. They disguise the final fields of trend mining; social community and social media; type; graph and community mining; purposes; privateness keeping; suggestion; function choice and relief; desktop studying; temporal and spatial facts; novel algorithms; clustering; biomedical information mining; move mining; outlier and anomaly detection; multi-sources mining; and unstructured info and textual content mining.

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Svd-based hierarchical data gathering for environmental monitoring. In: Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, UbiComp 2013 Adjunct, pp. 9–12. ACM, New York (2013) 5. : Tensor decompositions and applications. SIAM Review 51(3), 455–500 (2009) 6. : Scalable tensor decompositions for multi-aspect data mining. In: ICDM (2008) 7. : Tensorsplat: Spotting latent anomalies in time. In: 2012 16th Panhellenic Conference on Informatics (PCI), pp.

In order to find out the groups of similar patterns, we use a scalable k-means implementation, in MapReduce, so that we cluster different malicious patterns, produced by the tensor decomposition. For the cluster, we choose to use the cosine similarity (or rather its inverse) as a distance measure. The cosine distance we used in this study is shown as similarity(p, q) = cos(θ) = pp·qq where p and q are pairs of columns of the factor matrices A, B or C, produced by the decomposition. ,GkC }} ← PrioritizeCov(c,λ) 1 2 3 4 5 6 7 8 9 10 11 */ */ */ After clustering, we obtain different groups of connections, as summarized by decomposing X.

Unbalanced Pattern (UP): Consider a pattern Y and an unbalanced concept hierarchy U of height ‘h’. A pattern is called unbalanced pattern, if the height of at least one of the item in Y is less than ‘h’. The notion of unbalanced-ness depends on how the heights of the nodes in the concept hierarchy are distributed. It can be noted that we consider a pattern as unbalanced pattern, if the height of at least one item is less than the height of unbalanced concept hierarchy. Suppose, all the items of a pattern are at the height, say k.

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