Knowledge discovery from data streams

Knowledge Discovery from Data Streams Introduction An Illustrative Example A World in Movement Data Mining and Data Streams Introduction to Data Streams Data Stream Models Basic Streaming Methods Illustrative Applications Change Detection Introduction Tracking Drifting Concepts Monitoring the Learning Process Final Remarks Maintaining Histograms from Data Streams Introduction Histograms from Data Streams The Partition Incremental Discretization (PiD) Algorithm Applications to Data Mining Evaluating Streaming Algorithms Introduction Learning from Data Streams Evaluation Issues Lessons Learned and Open Issues Clustering from Data Streams Introduction Clustering Examples Clustering Variables Frequent Pattern Mining Introduction to Frequent Itemset Mining Heavy Hitters Mining Frequent Itemsets from Data Streams Sequence Pattern Mining Decision Trees from Data Streams Introduction The Very Fast Decision Tree Algorithm Extensions to the Basic Algorithm OLIN: Info-Fuzzy Algorithms Novelty Detection in Data Streams Introduction Learning and Novelty Novelty Detection as a One-Class Classification Problem Learning New Concepts The Online Novelty and Drift Detection Algorithm Ensembles of Classifiers Introduction Linear Combination of Ensembles Sampling from a Training Set Ensembles of Trees Adapting to Drift Using Ensembles of Classifiers Mining Skewed Data Streams with Ensembles Time Series Data Streams Introduction to Time Series Analysis Time Series Prediction Similarity between Time Series Symbolic Approximation (SAX) Ubiquitous Data Mining Introduction to Ubiquitous Data Mining Distributed Data Stream Monitoring Distributed Clustering Algorithm Granularity Final Comments The Next Generation of Knowledge Discovery Where We Want to Go Appendix: Resources Bibliography Index