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David L. Olson, Desheng Wu (auth.), Xue Li, Shuliang Wang,'s Advanced Data Mining and Applications: First International PDF

By David L. Olson, Desheng Wu (auth.), Xue Li, Shuliang Wang, Zhao Yang Dong (eds.)

ISBN-10: 354027894X

ISBN-13: 9783540278948

This ebook constitutes the refereed court cases of the 1st foreign convention on complex information Mining and purposes, ADMA 2005, held in Wuhan, China in July 2005.

The convention was once all in favour of subtle options and instruments which could deal with new fields of knowledge mining, e.g. spatial facts mining, biomedical information mining, and mining on high-speed and time-variant information streams; a variety of information mining to new functions is usually strived for. The 25 revised complete papers and seventy five revised brief papers provided have been conscientiously peer-reviewed and chosen from over six hundred submissions. The papers are geared up in topical sections on organization ideas, type, clustering, novel algorithms, textual content mining, multimedia mining, sequential facts mining and time sequence mining, internet mining, biomedical mining, complicated functions, protection and privateness matters, spatial information mining, and streaming facts mining.

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Read or Download Advanced Data Mining and Applications: First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005. Proceedings PDF

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Extra resources for Advanced Data Mining and Applications: First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005. Proceedings

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Cn } from the training set. For each testing document, a vector of rank values, F , representing the testing document D is calculated. We search for the category C such that it has the highest rank correlation coefficients with the testing document D. Then the document is assigned to the category C. 2 Classification Based on the Term Distances Another similarity function we propose is based on the intuition that the distance between two terms in the term graph reflects the relations between the terms.

4 Leap Approach There are two issues in frequent itemset mining: relevant itemset enumeration and counting the exact frequencies. Discovering all frequent patterns from the non-redundant sets such as the maximal patterns, does not necessarily mean we get de facto their exact counts. Moreover, every superfluous enumeration and counting of an itemset doomed infrequent is definitely time-consuming and useless in the final result. The idea of a leap traversal of the search space is, rather than systematically traverse the graph top-down or bottom-up, to cunningly jump from one node to the other avoiding as much as possible the superfluous nodes doomed irrelevant.

We propose the following heuristics to solve this problem, as shown in Figure . category category document document (a) Union Set (c) Smaller Set (b) Bigger Set category category document document (d) Intersection Fig. 2. Four Heuristics to Generate the Rank Correlation Cofficient Term Graph Model for Text Classification 25 1. Union Set. In this heuristic, we consider all the terms from the document and the category. In order to calculate the rank correlation, we need to assign rank values to terms that do not appear in the document (or the category).

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Advanced Data Mining and Applications: First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005. Proceedings by David L. Olson, Desheng Wu (auth.), Xue Li, Shuliang Wang, Zhao Yang Dong (eds.)


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