Download e-book for kindle: Active mining: new directions of data mining by Hiroshi Motoda

By Hiroshi Motoda

ISBN-10: 158603264X

ISBN-13: 9781586032647

ISBN-10: 4274905217

ISBN-13: 9784274905216

The necessity for gathering suitable information resources, mining worthy wisdom from various sorts of facts assets and swiftly reacting to scenario swap is ever expanding. lively mining is a suite of actions each one fixing part of this desire, yet jointly attaining the mining aim throughout the spiral influence of those interleaving 3 steps. This publication is a joint attempt from top and energetic researchers in Japan with a subject matter approximately energetic mining and a well timed record at the leading edge of knowledge assortment, user-centered mining and consumer interaction/reaction. It deals a modern assessment of recent ideas with real-world functions, stocks hard-learned stories, and sheds gentle on destiny improvement of energetic mining.

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Extra info for Active mining: new directions of data mining

Example text

Iii. If no matched region, go to 1. Fig. 3 shows interface of PUM. The window consists of three sub-windows: a Web browser window, an URL window and a training example window. A Web browser window (lower right in Fig. 3) shows a Web page in the same way to a Web browser and a user can easily indicate a region by highlighting it using a mouse. An URL window(upper in Fig. 3) stands for URLs of updated pages. A training example window (lower left in Fig. 3) indicates a, table of attribute and value of stored training examples.

124–132 (1995) 40 M. Okabe and S. Yamada / Interactive Weh Page Retrieval [3] Furnkranz. : Separate-and-Conquer Rule Learning. Artificial Intelligence Review. 13. : A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. Proc. : Some aspects of proximity searching in text retrieval system. Journal of Information Science. 18. 2. 89–98 (1992) [6] M. Okabe and S. Yamada: Interactive Document Retrieval with Relational Learning. Proc. 27–31 (2001) [7] Quinlan. : Induction of Logic Programs: FOIL and Related Systems.

Thus PUM automatically generates negative example for region identification to improve learning efficiency. We consider the neighborhood of an indicated region was a near miss example. Hence PUM generates negative examples from four regions: left, right, upper and lower regions to an indicated region. We experimentally found out this strategy is effective to make learning more efficient. 5. Yamada and Y. 3 47 Training examples for update check Training examples for UC are generated from the last Web page and an updated Web page, and described with the following attributes.

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Active mining: new directions of data mining by Hiroshi Motoda


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