By A. Bifet
This booklet 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 diverse features of the matter, selecting examine possibilities and extending the scope for purposes. it is usually an in-depth research of move mining and a theoretical research of proposed tools and algorithms. the 1st part is anxious with using an adaptive sliding window set of rules (ADWIN). on the grounds that this has rigorous functionality promises, utilizing it as opposed to counters or accumulators, it bargains the opportunity of extending such promises to studying and mining algorithms no longer at the start designed for drifting info. trying out with a number of tools, together with NaÃ¯ve Bayes, clustering, choice timber and ensemble equipment, is mentioned in addition. the second one a part of the booklet describes a proper examine of hooked up acyclic graphs, or timber, from the viewpoint of closure-based mining, offering effective algorithms for subtree checking out and for mining ordered and unordered widespread closed bushes. finally, a common technique to spot closed styles in an information circulation is printed. this is often utilized to enhance an incremental procedure, a sliding-window established approach, and a mode that mines closed bushes adaptively from info 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
42 CHAPTER 3. MINING EVOLVING DATA STREAMS • Type II: Estimator with Change Detector. An example is the Kalman Filter together with a CUSUM test change detector algorithm, see for example [JMJH04]. • Type III: Estimator with Memory. We add Memory to improve the results of the Estimator. For example, one can build an Adaptive Kalman Filter that uses the data in Memory to compute adequate values for the process variance Q and the measure variance R. In particular, one can use the sum of the last elements stored into a memory window to model the Q parameter and the difference of the last two elements to estimate parameter R.
36 CHAPTER 3. MINING EVOLVING DATA STREAMS The window adaptive approach that employs this method, works that way: at batch t, it essentially tries various windows sizes, training a SVM for each resulting training set. For each window size it computes a ξα-estimate based on the result of training, considering only the last batch for the estimation, that is the m most recent training examples z(t,1), . . , z(t,m). This reﬂects the assumption that the most recent examples are most similar to the new examples in batch t + 1.
To study the performance under this special case and to modify CMTreeMiner to handle it is a topic for future work. In this book we will propose closed frequent mining methods for unlabeled trees, that will outperform CMTreeMiner precisely in this case. 2 D RYADE PARENT Termier et al. proposed D RYADE PARENT [TRS+08] as a closed frequent attribute tree mining method comparable to CMTreeMiner. Attribute trees are trees such that two sibling nodes cannot have the same label. They extend to induced subtrees their previous algorithm D RYADE [TRS04].