By Rainer Schmidt, Tina Waligora (auth.), Petra Perner (eds.)
The commercial convention on information Mining ICDM-Leipzig was once the 6th occasion in a chain of annual occasions which began in 2000. we're happy to notice that the subject information mining with exact emphasis on real-world functions has been followed through such a lot of researchers around the globe into their learn paintings. We obtained 156 papers from 19 various international locations. the most issues are facts mining in drugs and advertising, net mining, mining of pictures and signs, theoretical points of knowledge mining, and facets of knowledge mining that package a chain of other info mining functions akin to intrusion detection, wisdom administration, production procedure regulate, time-series mining and legal investigations. this system Committee labored demanding in an effort to decide upon the easiest papers. The popularity price was once 30%. these kind of chosen papers are released during this complaints quantity as lengthy papers as much as 15 pages. furthermore we put in a discussion board the place paintings in development was once awarded. those papers are accumulated in a distinct poster complaints quantity and express once again the potentials and engaging advancements of knowledge mining for various functions. 3 new workshops were proven in reference to ICDM: (1) Mass info research on photographs and signs, MDA 2006; (2) info Mining for all times Sciences, DMLS 2006; and (3) information Mining in advertising, DMM 2006. those workshops are constructing new issues for information mining lower than the point of the exact software. we're happy to determine what percentage fascinating advancements are happening in those fields.
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Extra info for Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14-15, 2006. Proceedings
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Optimal value of DDP, α*, plotted against K for all benchmark datasets The steeper decrease of α* as K increases in the SMA implies that the measure of relevance used in the SMA fails to capture the relevance of a feature when K is large. In the OVA scheme, the decrease of α* as K increases is more gradual, implying better effectiveness than the SMA in capturing relevance for datasets with larger K. Furthermore, for all datasets, the value of α* in the OVA scheme is greater than or equal to the value of α* in the SMA.
An alignment-free comparison method is therefore of great value as it reduces the technical constraints as only pairwise comparisons are necessary, and is free of bias. Non-alignment methods are designed to compare each pair unrelated to other pairwise comparisons, and the distortion measures can compute pair-wise sequence similarity in such fashion. Given an appropriate numerical representation of DNA sequences, the performance of the new approach for DNA sequence comparison has been found to be better than that of other existing non-alignment methods.