This compendium is a completely revised version of an earlier book, Data Mining in Time Series Databases, by the same editors. It provides a unique collection of new articles written by leading experts that account for the latest developments in the field of time series and data stream mining.
The emerging topics covered by the book include weightless neural modeling for mining data streams, using ensemble classifiers for imbalanced and evolving data streams, document stream mining with active learning, and many more. In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in Data Science, Big Data, and related areas. Existing titles do not provide sufficient information on this topic.
Readership: Researchers, academics, professionals and graduate students in artificial intelligence, machine learning, databases, and information science.
Table of Contents
Chapter 1. Streaming Data Mining with Massive Online Analytics (MOA)
Chapter 2. Weightless Neural Modeling for Mining Data Streams
Chapter 3. Ensemble Classifiers for Imbalanced and Evolving Data Streams
Chapter 4. Consensus Learning for Sequence Data
Chapter 5. Clustering-Based Classification of Document Streams with Active Learning
Chapter 6. Supporting the Mining of Big Data by Means of Domain Knowledge During the Pre-mining Phases
Chapter 7. Data Analytics: Industrial Perspective & Solutions for Streaming Data