By Hiroshi Motoda
The necessity for accumulating appropriate info resources, mining valuable wisdom from various varieties of information resources and quickly reacting to scenario swap is ever expanding. lively mining is a suite of actions every one fixing part of this desire, yet jointly attaining the mining goal during the spiral influence of those interleaving 3 steps. This e-book is a joint attempt from best and energetic researchers in Japan with a subject approximately energetic mining and a well timed record at the leading edge of information assortment, user-centered mining and consumer interaction/reaction. It deals a latest assessment of contemporary ideas with real-world functions, stocks hard-learned reports, and sheds mild on destiny improvement of lively mining.
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Additional info for Active Mining - New Directions of Data Mining (Frontiers in Artificial Intelligence and Applications, Knowl)
15] Takama, Y. and Ishizuka, "FISH VIEW System: A Document Ordering Support System Employing Concept-structure-based Viewpoint Extraction," J. of Information Processing Society of Japan (IPSJ), 42, 7, 2000 (written in Japanese).  Takasugi, K. /. of Japanese Society for Artificial Intelligence, 14, 3, pp. 495 503. 1999 (written in Japanese). , "Visual Text Mining," J. of Japanese Society for Artificial Intelligence. 16, 2. pp. 226–232, 2001 (written in Japanese).  Zamir, O. , "Grouper: A Dynamic Clustering Interface to Web Search Results," Proc.
First PUM extracts training examples for both of region identification and update check from a region indicated by a user. Then a relational learning system automatically acquires two kinds of rules for region identification and update check. After such rules were generated, PUM becomes able to identify partial updates and determine whether it is one which a user wants to know or not by using two kinds of rules. If PUM decides an update is useful to a user, it notifies the update to a user. Otherwise PUM indicates the updated Web page to a user and obtains his/her evaluation.
Topic 12 is an example that filtering rules worked most effectively. The objective of topic 12 is "to identify a specific airport and describe the security measures already in effect or proposed for use at that airport". Search engine returns many non-relevant pages which introduce "the security which travelers must prepare". Removing such pages by filtering rules, our system could provide proper results. Table 1 shows the filtering rules generated for this topic. These rules represent the pages which introduce specific security systems by using the words "faa" and "screening".