Adaptive Stream Mining: Pattern Learning and Mining from by A. Bifet PDF

By A. Bifet

ISBN-10: 1607500906

ISBN-13: 9781607500902

This ebook is an important contribution to the topic of mining time-changing facts streams and addresses the layout of studying algorithms for this goal. It introduces new contributions on numerous various elements of the matter, selecting study possibilities and extending the scope for purposes. additionally it is 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 rather than counters or accumulators, it deals the opportunity of extending such promises to studying and mining algorithms no longer in the beginning designed for drifting information. trying out with a number of tools, together with Naïve Bayes, clustering, choice bushes and ensemble tools, is mentioned to boot. the second one a part of the e-book describes a proper research of attached acyclic graphs, or timber, from the perspective of closure-based mining, featuring effective algorithms for subtree trying out and for mining ordered and unordered common closed bushes. finally, a basic method to spot closed styles in a knowledge flow is printed. this is often utilized to improve an incremental approach, a sliding-window dependent approach, and a style that mines closed timber adaptively from information streams. those are used to introduce category equipment for tree info streams.

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Additional info for Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams

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Asai et al. developed FREQT. It uses an extension approach based on the rightmost path. FREQT uses an occurrence list base approach to determine the support of trees. • Rooted Unordered Trees – uFreqt [NK03]: Nijssen et al. extended FREQT to the unordered case. Their method solves in the worst case, a maximum bipartite matching problem when counting tree supports. – uNot [AAUN03]: Asai et al. presented uNot in order to extend FREQT. It uses an occurrence list based approach wich is similar to Zaki’s TreeMiner.

1 CMTreeMiner Chi et al. proposed CMTreeMiner [CXYM01], the first algorithm to discover all closed and maximal frequent labeled induced subtrees without first discovering all frequent subtrees. CMTreeMiner is to our knowledge, the state of art method for closed frequent tree mining. It shares many features with CloseGraph, and uses two pruning techniques: the left-blanket and right-blanket pruning. The blanket of a tree is defined as the set of immediate supertrees that are frequent, where an immediate supertree of a tree t is a tree that has one more vertex than t.

Vk(t)}, vi(t) = 1 i Their framework covers gradual and abrupt changes. Our approach is more concrete, we begin by dealing with a simple scenario: a data stream and two different concepts. Later, we will consider the general case with more than one concept drift events. Considering data streams as data generated from pure distributions, we can model a concept drift event as a weighted combination of two pure distributions that characterizes the target concepts before and after the drift. In our framework, we need to define the probability that every new instance of the stream belongs to the new concept after the drift.

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Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams by A. Bifet

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