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By Hiroshi Motoda

ISBN-10: 0585458901

ISBN-13: 9780585458908

ISBN-10: 158603264X

ISBN-13: 9781586032647

The necessity for accumulating correct facts assets, mining worthwhile wisdom from various types of facts assets and briskly reacting to state of affairs swap is ever expanding. lively mining is a suite of actions every one fixing part of this want, yet jointly reaching the mining goal throughout the spiral influence of those interleaving 3 steps. This ebook is a joint attempt from prime and energetic researchers in Japan with a topic approximately lively mining and a well timed record at the vanguard of information assortment, user-centered mining and consumer interaction/reaction. It bargains a modern evaluate of recent suggestions with real-world functions, stocks hard-learned reviews, and sheds mild on destiny improvement of lively mining.

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19] Zamir, O. and Etzioni. " Proc. SIGIR'98. pp. 46–54, 1998. This page intentionally left blank Active Mining H. ) IOS Press, 2002 Interactive Web page Retrieval with Relational Learning based Filtering Rules Masayuki Okabe okabe@mm. media, kyoto-u. jp CISS, IGSSE, Tokyo Institute of Technology 4259 Nagatuta-Cho, Midori-ku, Yokohama 226-8502, JAPAN Abstract. WWW Search Engines usually return a hit-list including many irrelevant pages because most of the users just input a few words as a query which is not enough to specify their information needs.

9), and the rule is established if it includes no negative training page (step2). The added literal is selected from a condition candidate set C. This C consists of the literals having all of the region-types and keywords in K as its arguments and being satisfied in training pages. Concretely the following two types of literals are used. • The ap literals having all of the region Jypes and keywords in K as its arguments and being satisfied in training pages. 36 M. Okabe and S. Yamada / Interactive Web Page Retrieval • The near literals having all of the region Jypcs and keywords in K as its argu ments and being satisfied in training pages.

Eval. No. 1 2 rule good <— •10/28(Sun) ' e clndex A T " '<->' '<+>' '<=>' '<+>' '20' '50' '<->' class good good good good good nogood nogood nogood Eval. No. 1 2 6 7 rule good «— . good «— . ' € dataNew. nogood <— good «— . nogood <— '50' € dataNew. € dataNew. nogood — good — . Case-2: stock market Web page Another example is executed in a stock market Web page shown in Fig.

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Active Mining by Hiroshi Motoda


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