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 complaints 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 awarded inside those lawsuits have been rigorously reviewed and chosen from 371 submissions. They conceal the overall fields of trend mining; social community and social media; class; graph and community mining; functions; privateness retaining; advice; function choice and aid; computer studying; temporal and spatial info; novel algorithms; clustering; biomedical info mining; circulation mining; outlier and anomaly detection; multi-sources mining; and unstructured info and textual content mining.

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Additional info for Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I (Lecture Notes in Computer Science)

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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.

In this case, the merging occurs quickly. For the pattern {coffee, orange}, the items coffee is mapped to category drinks and item orange maps to the category fruits. Further, both the categories drinks and fruits are mapped to the category fresh food, and the category fresh food in turn maps to root. We say that the pattern {coffee, orange} is more diverse than the pattern {tea, juice} as the merging is relatively slow in case of {coffee, orange} as compared to {tea, juice}. Consider the pattern {milk, battery} which is relatively more diverse than the pattern {coffee, orange} as both items merge at the root.

We define the projection of extended unbalanced concept hierarchy for Y as follows. Definition 6. Projection of Extended Unbalanced Concept Hierarchy of Y (P(Y/E)): Let Y be UP, U be unbalanced concept hierarchy, and E be the corresponding extended unbalanced concept hierarchy of U. The projection of E for the unbalanced pattern Y is P (Y /E). The P (Y /E) contains the portion of U which includes all the paths of the items of Y from the root. It can be noted that, in addition to real nodes/edges, P (Y /E) may contain dummy nodes/edges.

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