By A. Bifet
This ebook is an important contribution to the topic of mining time-changing facts streams and addresses the layout of studying algorithms for this objective. It introduces new contributions on a number of assorted elements of the matter, determining examine possibilities and lengthening the scope for purposes. additionally it is an in-depth research of movement 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). given that this has rigorous functionality promises, utilizing it instead of counters or accumulators, it bargains the potential for extending such promises to studying and mining algorithms now not in the beginning designed for drifting facts. trying out with numerous tools, together with Na??ve Bayes, clustering, determination timber and ensemble tools, is mentioned to boot. the second one a part of the booklet describes a proper research of hooked up acyclic graphs, or timber, from the perspective of closure-based mining, proposing effective algorithms for subtree checking out and for mining ordered and unordered widespread closed bushes. finally, a common technique to spot closed styles in a knowledge move is printed. this is often utilized to boost an incremental strategy, a sliding-window established strategy, and a style that mines closed bushes adaptively from facts streams. those are used to introduce class equipment for tree facts streams.IOS Press is a global technology, technical and clinical writer of top quality books for lecturers, scientists, and pros in all fields. many of the components we submit in: -Biomedicine -Oncology -Artificial intelligence -Databases and knowledge structures -Maritime engineering -Nanotechnology -Geoengineering -All elements of physics -E-governance -E-commerce -The wisdom financial system -Urban reviews -Arms keep an eye on -Understanding and responding to terrorism -Medical informatics -Computer Sciences
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Additional resources for Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
PRELIMINARIES sentation and correlation structure detection, applying a linear regression model in the wavelet domain. 3 Clustering in data streams An incremental k-means algorithm for clustering binary data streams was proposed by Ordonez [Ord03]. As this algorithm has several improvements to k-means algorithm, the proposed algorithm can outperform the scalable k-means in the majority of cases. The use of binary data simpliﬁes the manipulation of categorical data and eliminates the need for data normalization.
The more time an algorithm has, the more likely it is that accuracy can be increased. In evolving data streams we are concerned about • evolution of accuracy • probability of false alarms • probability of true detections • average delay time in detection Sometimes, learning methods do not have change detectors implemented inside, and then it may be hard to deﬁne ratios of false positives and negatives, and average delay time in detection. In these cases, learning curves 46 CHAPTER 3. MINING EVOLVING DATA STREAMS may be a useful alternative for observing the evolution of accuracy in changing environments.
CHAPTER 2. PRELIMINARIES 18 Drift Detection Method The drift detection method (DDM) proposed by Gama et al. [GMCR04] controls the number of errors produced by the learning model during prediction. It compares the statistics of two windows: the ﬁrst one contains all the data, and the second one contains only the data from the beginning until the number of errors increases. This method does not store these windows in memory. It keeps only statistics and a window of recent data. The number of errors in a sample of n examples is modelized by a binomial distribution.