Jiawei Han, Hector Gonzalez, Xiaolei Li, Diego Klabjan's Advanced Data Mining and Applications: Second International PDF

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 info Mining and functions, ADMA 2006, held in Xi'an, China in August 2006.

The forty-one revised complete papers and seventy four revised brief papers awarded including four invited papers have been conscientiously reviewed and chosen from 515 submissions. The papers are prepared in topical sections on organization ideas, type, clustering, novel algorithms, textual content mining, multimedia mining, sequential info mining and time sequence mining, net mining, biomedical mining, complicated purposes, defense and privateness matters, spatial facts mining, and streaming facts mining.

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Additional info for Advanced Data Mining and Applications: Second International Conference, ADMA 2006, Xi’an, China, August 14-16, 2006 Proceedings

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This distinct property requires us to develop new methods to construct a workflow cube that has aggregation on both item and path dimensions. 3 Workflow Redundancy The workflow registered for a given cell in a workflow cube may not provide new information on the characteristics of the data in the cell, if the cells at a higher abstraction level on the item lattice, and the same abstraction level on the path lattice, can be used to derive the workflow in the cell. For example, if we have a workflow G1 for milk, and a workflow G2 from milk 2% (milk is an ancestor of milk 2% in the item abstraction lattice), and G1 = G2 , we view that G2 is redundant, as it can be inferred from G1 .

Thus, with rj we will refer to the input vector for SOM, SOM-SD or CSOM-SD representing the root of the XML document dj . The R index is computed as follows: the mapping of every node in the dataset is computed; then for every neuron i the set win(i) of root nodes for which it was a winner is computed. Let wint (i) = {rj |rj ∈ win(i) and yj = t}, the t (i)| value Ri = maxt |win |win(i)| is computed for neurons with |win(i)| > 0 and the 1 index R computed as R = W i,|win(i)|>0 Ri , where W = i,|win(i)|>0 1 is the total number of neurons which were activated at least once by a root node.

Such applications are considered as a future task. References 1. M. Hagenbuchner, A. Sperduti, and A. Tsoi. A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks, 14(3):491– 505, May 2003. 2. M. Hagenbuchner, A. Sperduti, and A. Tsoi. Contextual processing of graphs using self-organizing maps. In European symposium on Artificial Neural Networks, Poster track, Bruges, Belgium, 27 - 29 April 2005. 3. T. Kohonen. Self-Organizing Maps, volume 30 of Springer Series in Information Sciences.

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