By A. Bifet

ISBN-10: 1607500906

ISBN-13: 9781607500902

This ebook is an important contribution to the topic of mining time-changing info streams and addresses the layout of studying algorithms for this function. It introduces new contributions on a number of diverse features of the matter, determining examine possibilities and extending the scope for purposes. it's also an in-depth research of move mining and a theoretical research of proposed tools and algorithms. the 1st part is worried with using an adaptive sliding window set of rules (ADWIN). considering the fact that this has rigorous functionality promises, utilizing it instead of counters or accumulators, it bargains the potential of extending such promises to studying and mining algorithms now not firstly designed for drifting information. checking out with a number of equipment, together with Na??ve Bayes, clustering, choice bushes and ensemble tools, is mentioned in addition. the second one a part of the booklet describes a proper research of hooked up acyclic graphs, or bushes, from the perspective of closure-based mining, proposing effective algorithms for subtree trying out and for mining ordered and unordered common closed bushes. finally, a basic technique to spot closed styles in a knowledge circulate is printed. this is often utilized to increase an incremental approach, a sliding-window dependent strategy, and a mode that mines closed timber adaptively from facts streams. those are used to introduce class tools for tree information streams.IOS Press is a world technological know-how, technical and scientific writer of fine quality books for lecturers, scientists, and pros in all fields. a few of the parts we put up in: -Biomedicine -Oncology -Artificial intelligence -Databases and knowledge structures -Maritime engineering -Nanotechnology -Geoengineering -All points of physics -E-governance -E-commerce -The wisdom financial system -Urban experiences -Arms keep watch over -Understanding and responding to terrorism -Medical informatics -Computer Sciences

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

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A METHODOLOGY FOR ADAPTIVE STREAM MINING 41 such as the variance. The only assumption on the distribution is that each xt is drawn independently from each other. Memory is the component where the algorithm stores all the sample data or summary that considers relevant at current time, that is, that presumably shows the current data distribution. The Estimator component is an algorithm that estimates the desired statistics on the input data, which may change over time. The algorithm may or may not use the data contained in the Memory.

There are several ways to construct such a hypothesis test. The simplest one is to study the difference μ ^0 − μ ^ 1 ∈ N(0, σ20 + σ21), under H0 or, to make a χ2 test (^ μ0 − μ ^ 1)2 ∈ χ2(1), under H0 2 σ0 + σ21 from which a standard hypothesis test can be formulated. 96 σ20 + σ21 Note that this test uses the normality hypothesis. In Chapter 4 we will propose a similar test with theoretical guarantees. 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.

The IFN algorithm is using the pre-pruning strategy: a node is split if this procedure brings about a statistically significant decrease in the entropy value (or increase in the mutual information) of the target attribute. If none of the remaining input attributes provides a statistically significant increase in mutual information, the network construction stops. The output of this algorithm is a network, which can be used to predict the values of a target attribute similarly to the prediction technique used in decision trees.

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


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