Mitsa Temporal Data Mining

1. Auflage 2010
ISBN: 978-1-4200-8977-6
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 395 Seiten

Reihe: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Temporal data mining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data today.

From basic data mining concepts to state-of-the-art advances, Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining.

Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references. In the appendices, the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purpose of characterizing a population. She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter. Check out the author's blog at http://theophanomitsa.wordpress.com/

Zielgruppe

Computer scientists, data mining researchers, graduate students in data mining, biomedical researchers, financial data analysts, business managers, geospatial data analysts, and web developers.

Autoren/Hrsg.

Mitsa, Theophano

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

Temporal Databases and Mediators
Time in Databases
Database Mediators
Temporal Data Similarity Computation, Representation, and Summarization
Temporal Data Types and Preprocessing
Time Series Similarity Measures
Time Series Representation
Time Series Summarization Methods
Temporal Event Representation
Similarity Computation of Semantic Temporal Objects
Temporal Knowledge Representation in Case-Based Reasoning Systems
Temporal Data Classification and Clustering
Classification Techniques
Clustering
Outlier Analysis and Measures of Cluster Validity
Time Series Classification and Clustering Techniques
Prediction
Forecasting Model and Error Measures
Event Prediction
Time Series Forecasting
Advanced Time Series Forecasting Techniques
Temporal Pattern Discovery
Sequence Mining
Frequent Episode Discovery
Temporal Association Rule Discovery
Pattern Discovery in Time Series
Finding Patterns in Streaming Time Series
Mining Temporal Patterns in Multimedia
Temporal Data Mining in Medicine and Bioinformatics
Temporal Pattern Discovery, Classification, and Clustering
Temporal Databases/Mediators
Temporality in Clinical Workflows
Temporal Data Mining and Forecasting in Business and Industrial Applications
Temporal Data Mining Applications in Enhancement of Business and Customer Relationships
Business Process Applications
Miscellaneous Industrial Applications
Financial Data Forecasting
Web Usage Mining
General Concepts
Web Usage Mining Algorithms
Spatiotemporal Data Mining
General Concepts
Finding Periodic Patterns in Spatiotemporal Data
Mining Association Rules in Spatiotemporal Data
Applications of Spatiotemporal Data Mining in Geography
Spatiotemporal Data Mining of Traffic Data
Spatiotemporal Data Reduction
Spatiotemporal Data Queries
Indexing Spatiotemporal Data Warehouses
Semantic Representation of Spatiotemporal Data
Historical Spatiotemporal Aggregation
Spatiotemporal Rule Mining for Location-Based Aware Systems
Trajectory Data Mining
The FlowMiner Algorithm
The TopologyMiner Algorithm
Applications of Temporal Data Mining in the Environmental Sciences
Appendices
Index
Bibliography and References appear at the end of each chapter.

Über Autor(innen)

Theophano Mitsa, Ph.D., is a software consultant and electrical engineer with expertise in image analysis, computer vision, machine learning, pattern recognition, medical informatics, and decision support systems.