By Hiroshi Motoda
The necessity for accumulating appropriate facts assets, mining precious wisdom from varied different types of facts resources and swiftly reacting to state of affairs switch is ever expanding. lively mining is a suite of actions each one fixing part of this desire, yet jointly attaining the mining aim during the spiral impact of those interleaving 3 steps. This booklet is a joint attempt from major and lively researchers in Japan with a subject approximately lively mining and a well timed file at the vanguard of information assortment, user-centered mining and consumer interaction/reaction. It bargains a latest assessment of recent strategies with real-world purposes, stocks hard-learned reports, and sheds mild on destiny improvement of energetic mining.
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Additional info for Active mining: new directions of data mining
Using the information gain, a system is able to select a literal which obtains not only much information for a training page but also many positive training pages satisfying it (step 17). This rule construction using information gain is efficient because it is greedy. However it sometimes selects bad literal and stops before completion. In such a case, if a current rule has some literals in its body, this algorithm eliminates all the literals in its body and restarts a rule making process. 4. 15).
For test questions, we used 20 topics (No. 401~-420) provided by the small web track in TREC-82 . This test collection is often used for evaluating the performance of retrieval systems in Information Retrieval community. Figure 4 is an example of topic which is composed of four parts. Title part consists of 1~3 words. We used these title words as a query for search engine. Relevance judgment of each page is conducted by the same searcher according to the account written in the description and the narrative part of each topic.
Initial search: A user inputs a query (a set of terms) to our Web search system. Then the system puts the query through to a search engine and obtains a hit-list. 2. Evaluation of results by a user: After getting a hit-list from a search engine, the system asks the user to evaluate and mark the relevancy (relevant or nonrelevant) of a small part of Web pages in the hit-list (usually upper 10 pages), and stores those pages as training pages, especially the relevant pages as positive training pages and the non-relevant pages as negative training pages.