By A. Bifet
This e-book is an important contribution to the topic of mining time-changing information streams and addresses the layout of studying algorithms for this function. It introduces new contributions on numerous various points of the matter, determining learn possibilities and extending the scope for functions. it is also an in-depth examine of movement mining and a theoretical research of proposed equipment and algorithms. the 1st part is anxious with using an adaptive sliding window set of rules (ADWIN). seeing that this has rigorous functionality promises, utilizing it as opposed to counters or accumulators, it deals the opportunity of extending such promises to studying and mining algorithms now not firstly designed for drifting info. checking out with a number of tools, together with NaÃ¯ve Bayes, clustering, choice timber and ensemble equipment, is mentioned to boot. the second one a part of the publication describes a proper learn of attached acyclic graphs, or timber, from the viewpoint of closure-based mining, providing effective algorithms for subtree checking out and for mining ordered and unordered widespread closed bushes. finally, a basic technique to spot closed styles in an information move is printed. this can be utilized to boost an incremental procedure, a sliding-window established process, and a mode that mines closed bushes adaptively from facts streams. those are used to introduce type equipment for tree information streams.
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Extra info for Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
However, we could have used this test on the methods of Chapter 4. The Kolmogorov-Smirnov test [Kan06] is another statistical test used to compare two populations. Given samples from two populations, the cumulative distribution functions can be determined and plotted. Hence the maximum value of the difference between the plots can be found and compared with a critical value. If the observed value exceeds the critical value, H0 is rejected and a change is detected. It is not obvious how to implement the Kolmogorov-Smirnov test dealing with data streams.
If HTδ is the tree produced by the Hoeffding tree algorithm with desired probability δ given inﬁnite examples, DT is the asymptotic batch tree, and p is the leaf probability, then E[Δi(HTδ, DT )] ≤ δ/p. VFDT (Very Fast Decision Trees) is the implementation of Hoeffding trees, with a few heuristics added, described in [DH00]; we basically identify both in this book. 1. Counts nijk are the sufﬁcient statistics needed to choose splitting attributes, in particular the information gain function G implemented in VFDT.
During the training phase the algorithm maintains a short term memory. Given a data stream, a limited number of the most recent examples are maintained in a data structure that supports constant time insertion and deletion. When a test is installed, a leaf is transformed into a decision node with two descendant leaves. The sufﬁcient statistics of the leaf are initialized with the examples in the short term memory that will fall at that leaf. 38 CHAPTER 3. 3. A METHODOLOGY FOR ADAPTIVE STREAM MINING 39 The UFFT algorithm maintains, at each node of all decision trees, a Na¨ıve Bayes classiﬁer.
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams by A. Bifet