Get Active mining: new directions of data mining PDF

By Hiroshi Motoda

The necessity for accumulating proper information assets, mining necessary wisdom from diverse types of facts resources and briskly reacting to scenario swap is ever expanding. energetic mining is a set of actions every one fixing part of this desire, yet jointly reaching the mining target throughout the spiral influence of those interleaving 3 steps. This booklet is a joint attempt from best and energetic researchers in Japan with a subject matter approximately lively mining and a well timed file at the vanguard of knowledge assortment, user-centered mining and person interaction/reaction. It deals a modern evaluate of recent ideas with real-world functions, stocks hard-learned reviews, and sheds mild on destiny improvement of lively mining.

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Additional info for Active mining: new directions of data mining

Sample text

Anchor : The region surrounded with anchor tags . For example, the . • head : The region surrounded with heading tags . • para : The region surrounded with paragraph tags

. This means the region of the same paragraph. We can represent various features of pages by combining these relations. Here is an example set of rules. { relevant :- ap(title, mobile), ap(anchor. PDA). relevant :- near(para, palm, os). Filtering rules are interpreted disjunction. Thus if any rule is satisfied in a Web page, the page will be considered relevant and otherwise non-relevant.

The number of a procedure corresponds to the number in Fig. 2. 1. Obtaining a monitor region. (a) A user indicates a region in which he/she wants to know the update by mouse highlight operation on interface of PUM(Fig. 3). (b) PUM obtains the region and analyses it. 2. Acquiring updates monitoring rules. (a) Generating two kinds of training examples. • Training examples for RI consisting of the following properties are generated. — HTML source code of a region indicated by a user. - A sequence of ancestors of the region in a HTML tree.

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.

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