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By Jiawei Han, Hector Gonzalez, Xiaolei Li, Diego Klabjan (auth.), Xue Li, Osmar R. Zaïane, Zhan-huai Li (eds.)
This publication constitutes the refereed court cases of the second one foreign convention on complex facts Mining and purposes, ADMA 2006, held in Xi'an, China in August 2006.
The forty-one revised complete papers and seventy four revised brief papers provided including four invited papers have been conscientiously reviewed and chosen from 515 submissions. The papers are equipped in topical sections on organization ideas, type, clustering, novel algorithms, textual content mining, multimedia mining, sequential facts mining and time sequence mining, net mining, biomedical mining, complex purposes, safeguard and privateness matters, spatial info mining, and streaming info mining.
Read or Download Advanced Data Mining and Applications: Second International Conference, ADMA 2006, Xi’an, China, August 14-16, 2006 Proceedings PDF
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Extra info for Advanced Data Mining and Applications: Second International Conference, ADMA 2006, Xi’an, China, August 14-16, 2006 Proceedings
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Keogh et al introduced a heuristic discord discovery algorithm based on the brute force algorithm and some observations [5]. They found that actually we do not need to find the nearest non-self match for each possible candidate subsequence. According to the definition of time series discord, a candidate cannot be a discord, if we can find any subsequence that is closer to the current candidate than the current smallest nearest non-self match distance. This basic idea successfully prunes away a lot of unnecessary searches and reduces a lot of computational time.
It is also observed that graphs belonging to different classes form clear clusters some of which are very small in size. This observation confirms the experimental findings which show that the SOM-SD will be able to generalize well. Figure 2(c) gives the mapping of the root nodes as produced by the CSOM-SD. Again, it is found that the largest portion of the map is filled in by neurons which are either not activated or are activated by nodes other than the labelled root nodes. Clear clusters are formed which are somewhat smaller in size when compared to those formed in the SOM-SD case.
Specifically, it was shown that the given learning problem depends on the availability of causal information about the XML tags within the original document in order to produce a good grouping or classification of the data. The incorporation of contextual information did not help to improve on the results further. The training set used in this paper featured a wide variety of tree structured graphs. We found that most graphs are relatively small in size, only few graphs were either very wide or featured many nodes.