By Hiroshi Motoda
The necessity for accumulating correct information resources, mining worthy wisdom from various sorts of facts resources and swiftly reacting to state of affairs switch is ever expanding. lively mining is a set of actions every one fixing part of this desire, yet jointly attaining the mining aim during the spiral impact of those interleaving 3 steps. This e-book is a joint attempt from prime and energetic researchers in Japan with a subject matter approximately energetic mining and a well timed record at the vanguard of information assortment, user-centered mining and consumer interaction/reaction. It deals a latest review of contemporary strategies with real-world purposes, stocks hard-learned stories, and sheds gentle on destiny improvement of lively mining.
Read or Download Active mining: new directions of data mining PDF
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Extra resources for Active mining: new directions of data mining
Therefore, it is meaningless to compare both methods in terms of coverage. In this paper, questionnaires are performed to compare the clusters generated by the proposed method and that by k-means. from the following viewpoints. • Coherence: how closely the documents within a cluster relate to each other. • Understandability: how easily the topic- of a cluster can be understood by users. The sets of documents used for the experiments are collected from the following online news sites. Setl Documents in entertainment category of Yahoo!
4 is described like the following. 2 table 1 tr 2 td 2 cNo rNo 1 2 data fine clndex rlndex tomorrow tokyo class good Negative examples for region identification Since relational learning is a kind of inductive learning, negative examples play a important role to avoid over-generalization. However, in such an interactive system like PUM, Obtaining negative many negative examples may force much cognitive load to a user. Thus PUM automatically generates negative example for region identification to improve learning efficiency.
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. PUM was implemented using Visual C++ and Ruby on Windows2000. 2 Region identification and update check PUM learns update monitoring rules to check partial updates in a Web page. The update monitoring rules consists of two kinds of rules: region identification rules and update check rules. Region identification (RI) rules are used to identify and extract a region in which a user wants to know its updates.
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