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

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 court cases have been conscientiously reviewed and chosen from 371 submissions. They disguise the overall fields of development mining; social community and social media; category; graph and community mining; functions; privateness keeping; suggestion; characteristic choice and aid; desktop studying; temporal and spatial info; novel algorithms; clustering; biomedical information mining; flow 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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The GP (Y, l, P (Y /C)) is obtained by replacing every item at level (l + 1) in GP (Y, l + 1, P (Y /C)) with its corresponding parent at the level ‘l’ with duplicates removed, if any. The notion of merging factor at level l is defined as follows. Merging factor (MF(Y, l, P(Y/C))): Let Y be BP and l be the height. The merging factor indicates how the items of a pattern merge from the level l + 1 to the level l (0 ≤ l < h). If there is no change, the MF(Y,l) is 1. If all items merges to one node, the MF(Y,l) value equals to 0.

In this paper, we have proposed a new interestingness measure to rank the patterns based on diversity. We have proposed a general approach to assign the drank to the patterns by considering unbalanced concept hierarchy. For computing the drank of a pattern, the unbalanced concept hierarchy is being converted into balanced concept hierarchy by adding dummy nodes and edges. The notion of adjustment factor is proposed to remove the effect of the dummy nodes and edges. The drank is calculated using the notions of merging factor, level factor and adjustment factor.

Top Diverse Patterns: In Table 3, we present the list of the top 3-item patterns ordered by drank. In this table, the first column shows the pattern, the second column shows the support of the pattern, the third column shows the drank of UP, the fourth column shows the drank of UP with extended concept hierarchy (E), and the final column shows the difference from drank of UP and the drank of UP with E. Top Frequent Patterns: Table 4 contains the list of top 3-item patterns ordered by support value.

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