By Hiroshi Motoda
The necessity for accumulating suitable information assets, mining priceless wisdom from diverse kinds of facts assets and swiftly 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 attaining the mining target during the spiral impact of those interleaving 3 steps. This booklet is a joint attempt from top and energetic researchers in Japan with a subject matter approximately energetic mining and a well timed file at the vanguard of knowledge assortment, user-centered mining and person interaction/reaction. It bargains a modern review of recent suggestions with real-world functions, stocks hard-learned stories, and sheds mild on destiny improvement of lively mining.
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Extra info for Active mining: new directions of data mining
This algorithm generates a filtering rule one by one, and adds the generated rule to R. When a rule is generated, the pages covered with the rule are removed from the set of positive training pages E+. Thus, as the number of generated filtering rules increases, E+ decreases, and the algorithm finishes if the E+ becomes empty (step3-5). 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.
PUM utilizes RIPPER as a relational learning system. RIPPER acquires rules to classify examples into two classes, and the learned rule is described with symbolic representation, not weight distribution of neurons in neural network learning. Thus a user can easily understand rules and modify them. The another advantage of RIPPER is that it efficiently learns rules. For interactive system like PUM, fast learning is necessary. RIPPER is given training example's consisting of attributes and their values.
The another advantage of RIPPER is that it efficiently learns rules. For interactive system like PUM, fast learning is necessary. RIPPER is given training example's consisting of attributes and their values. It is able to deal with a nominal value, a set value and a continuous value5 as an attribute value1. At step 2a in procedures of the last subseeition, PUM generates twe> kinds of training examples for learning RI rules and UC rules. In the folk)wing, we explain representation of such training examples.