Temporal Data Mining

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A01=Theophano Mitsa
advanced temporal pattern discovery
Apriori Algorithm
Author_Theophano Mitsa
Business Processes
Category=UMB
Category=UNF
Data Sets
DTW
Embedding Dimension
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eq_computing
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Execution Time
Financial Time Series
Financial Time Series Forecasting
Frequent Itemsets
Gene Expression Time Series
Gene Expression Time Series Data
Gps Trace
health data analytics
organisational data insights
Pattern Discovery Algorithms
population characterisation
RFID Data
Spatiotemporal Data
Spatiotemporal Data Mining
spatiotemporal modelling
TAs
Temporal Data Mining
Temporal Databases
Temporal Queries
time series analysis
Time Series Classification
Time Series Forecasting
Time Series Prediction
Wavelet Transform
web behaviour mining
Web Usage Mining

Product details

  • ISBN 9781420089769
  • Weight: 780g
  • Dimensions: 156 x 234mm
  • Publication Date: 10 Mar 2010
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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/

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.

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