Intelligence Semantics

Active mining: new directions of data mining by Hiroshi Motoda PDF

By Hiroshi Motoda

The necessity for accumulating appropriate information assets, mining important wisdom from diverse types of facts assets and quickly reacting to scenario switch is ever expanding. energetic mining is a set of actions every one fixing part of this want, yet jointly attaining the mining target in the course of the spiral impression of those interleaving 3 steps. This booklet is a joint attempt from top and lively researchers in Japan with a topic approximately lively mining and a well timed record at the leading edge of knowledge assortment, user-centered mining and consumer interaction/reaction. It deals a modern review of recent strategies with real-world functions, stocks hard-learned studies, and sheds gentle on destiny improvement of energetic mining.

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The results showed that our system enables us to find more relevant pages though the effect differs in every questions. Our system need quick response and moderate machine power. Thus it should be a user side application because search engines cannot afford to attach such a function. One of the future problem is to reduce the cost which users need to judge pages. We plan to apply clustering methods for this problem. References [1] Baeza-Yates, R. : Modern Information Retrieval: Addison-Wesley, Wokingham, UK, (1999) [2] Cohen.

Nogood — good — . Case-2: stock market Web page Another example is executed in a stock market Web page shown in Fig. 5. In this example, after several evaluation of a user. PUM became able to correctly notify partial updates of a cell indicating a stock value of a particular company. Also PUM is available to more complicated Web page with two tables shown in Fig. 6. 1 Discussion Direct modification of miles by a user Though RIPPER is a fast learning system, the learning is not sufficiently rapid for an interactive learning system like PUM.

Html. [8] P. Resnick, N. lacovou. M. Suchak. P. Bergstrom. and J. Riedl. Grouplens: An open architechture for collaborative filtering of net news. In CSCW '94- pages 175 186. 1994. [9] U. Shardanand and P. Maes. Social information filtering: Algorithms for automating "word of mouth". In CHI. pages 210 217. 1997. Active Mining H. ) 1OS Press, 2002 Immune Network-based Clustering for WWW Information Gathering/Visualization Yasufumi Takarna1'2 and Kaoru Hirota 1 {takama,hirota}@hrt. jp 1 Tokyo Institute of Technology 4259 Nagatsuta, Midori-ku, Yokohama 226-8502 JAPAN 2 PREST, Japan Science and Technology Corporation.

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