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 information Mining, PAKDD 2014, held in Tainan, Taiwan, in may possibly 2014. The forty complete papers and the 60 brief papers provided inside those complaints have been rigorously reviewed and chosen from 371 submissions. They conceal the final fields of development mining; social community and social media; type; graph and community mining; purposes; privateness keeping; advice; characteristic choice and relief; computing device studying; temporal and spatial information; novel algorithms; clustering; biomedical info mining; movement mining; outlier and anomaly detection; multi-sources mining; and unstructured info and textual content mining.
Read or Download Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I PDF
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Extra resources for Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I
A scalable network forensics mechanism for stealthy self-propagating attacks. Computer Communications (2013) 9. : Iterative incremental clustering of time series. , B¨ ohm, K. ) EDBT 2004. LNCS, vol. 2992, pp. 106–122. Springer, Heidelberg (2004) 10. : Fast mining and forecasting of complex time-stamped events. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, pp. 271–279. ACM, New York (2012) 11. : Optimal multi-scale patterns in time series streams.
That is, one pattern may quickly merge to few higher level items within few levels and the other pattern may merge to few higher level items by crossing more number of levels. By capturing the process of merging, we deﬁne the notion of diverse rank (drank ). So, drank (Y ) is calculated by capturing how the items are merged from leaf-level to root in P (Y /C). It can be observed that a given pattern maps from the leaf level to the root level through a merging process by crossing intermediate levels.
Given UP and the corresponding unbalanced concept hierarchy U , the following steps should be followed to calculate the drank of UP. (i) Convert the U to the corresponding extended U . (ii) Compute the eﬀect of the dummy nodes and edges. (iii) Compute the drank. Extracting Diverse Patterns with Unbalanced Concept Hierarchy 21 root Level 0 Level 1 milk drinks Level 2 original Level 3 fat no-fat Level 4 whole milk cola flavor * hair beauty soft drinks * * * juice * fresh juice * * * * * * 2% fat- mango badam pepsi coke orga- apple mango grape sham- hair hair milk free milk milk nic juice juice juice poo spray oil milk juice Fig.
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