Title page -- Contents -- Introduction and Preliminaries -- Introduction -- Data Mining -- Data stream mining -- Frequent tree pattern mining -- Overview of the book -- Preliminaries -- Classification and Clustering -- Naïve Bayes -- Decision Trees -- k-means clustering -- Change Detection and Value Estimation -- Change Detection -- Estimation -- Frequent Pattern Mining -- Mining data streams: state of the art -- Sliding Windows in data streams -- Classification in data streams -- Clustering in data streams -- Frequent pattern mining: state of the art -- CMTreeMiner -- DryadeParent -- Streaming Pattern Mining -- Evolving Data Stream Learning -- Mining Evolving Data Streams -- Introduction -- Theoretical approaches -- Algorithms for mining with change -- FLORA: Widmer and Kubat -- Suport Vector Machines: Klinkenberg -- OLIN: Last -- CVFDT: Domingos -- UFFT: Gama -- A Methodology for Adaptive Stream Mining -- Time Change Detectors and Predictors: A General Framework -- Window Management Models -- Optimal Change Detector and Predictor -- Experimental Setting -- Concept Drift Framework -- Datasets for concept drift -- MOA Experimental Framework -- Adaptive Sliding Windows -- Introduction -- Maintaining Updated Windows of Varying Length -- Setting -- First algorithm: ADWIN0 -- ADWIN0 for Poisson processes -- Improving time and memory requirements -- Experimental Validation of ADWIN -- Example 1: Incremental Naïve Bayes Predictor -- Experiments on Synthetic Data -- Real-world data experiments -- Example 2: Incremental k-means Clustering -- Experiments -- K-ADWIN = ADWIN + Kalman Filtering -- Experimental Validation of K-ADWIN -- Example 1: Naïve Bayes Predictor -- Example 2: k-means Clustering -- K-ADWIN Experimental Validation Conclusions -- Time and Memory Requirements -- Decision Trees -- Introduction -- Decision Trees on Sliding Windows.

HWT-ADWIN: Hoeffding Window Tree using ADWIN -- CVFDT -- Hoeffding Adaptive Trees -- Example of performance Guarantee -- Memory Complexity Analysis -- Experimental evaluation -- Time and memory -- Ensemble Methods -- Bagging and Boosting -- New method of Bagging using trees of different size -- New method of Bagging using ADWIN -- Adaptive Hoeffding Option Trees -- Comparative Experimental Evaluation -- Closed Frequent Tree Mining -- Mining Frequent Closed Rooted Trees -- Introduction -- Basic Algorithmics and Mathematical Properties -- Number of subtrees -- Finding the intersection of trees recursively -- Finding the intersection by dynamic programming -- Closure Operator on Trees -- Galois Connection -- Level Representations -- Subtree Testing in Ordered Trees -- Mining Frequent Ordered Trees -- Unordered Subtrees -- Subtree Testing in Unordered Trees -- Mining frequent closed subtrees in the unordered case -- Closure-based mining -- Induced subtrees and Labeled trees -- Induced subtrees -- Labeled trees -- Applications -- Datasets for mining closed frequent trees -- Intersection set cardinality -- Unlabeled trees -- Labeled trees -- Mining Implications from Lattices of Closed Trees -- Introduction -- Itemsets association rules -- Classical Propositional Horn Logic -- Association Rules -- On Finding Implicit Rules for Subtrees -- Experimental Validation -- Evolving Tree Data Stream Mining -- Mining Adaptively Frequent Closed Rooted Trees -- Relaxed support -- Closure Operator on Patterns -- Closed Pattern Mining -- Incremental Closed Pattern Mining -- Closed pattern mining over a sliding window -- Adaptive closed pattern mining -- Closed pattern mining in the presence of distribution change -- Closed Tree Mining Application -- Incremental Closed Tree Mining -- Experimental Evaluation -- Unlabeled Trees -- Labeled Trees.

Adaptive XML Tree Classification -- Introduction -- Classification using Compressed Frequent Patterns -- Closed Frequent Patterns -- Maximal Frequent Patterns -- XML Tree Classification framework on data streams -- Adaptive Tree Mining on evolving data streams -- Experimental evaluation -- Bibliography.