Temporal Data Mining

Temporal Data Mining

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Features

  • Covers state-of-the-art applications in medicine/bioinformatics, business and finance forecasting, web usage mining, and spatiotemporal knowledge discovery
  • Uses illustrative examples to explain basic data mining concepts
  • Discusses essential topics in temporal data mining, including temporal databases, similarity computation, classification, clustering, prediction, and temporal pattern discovery
  • Helps readers choose the most suitable temporal data mining algorithm to process their data
  • Contains programs written in Java language that implement some of the algorithms

Summary

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/

Table of Contents

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.

Author Bio(s)

Editorial Reviews

The book would be enlightening for a statistical reader wishing to learn about the development of more empirical, less formal, methods in parallel to the work being done by the statistical community.
—David J. Hand, International Statistical Review (2011), 79

I very strongly recommend [this] monumental monograph for the classroom as a graduate text or as a standalone [book] for professionals such as engineers and scientists for their research. Dr. Mitsa present[s] the latest developments of data mining in the time domain with extreme simplicity and elegance while offering in-depth exposure to the principles and applications of temporal data mining.
In my research, I find this knowledge particularly useful for remote sensing applications, specifically, in the detection, classification, characterization and imaging of distant objects as well as for the detection, characterization, monitoring, and staging of early cancer cells with high discrimination potential and low false-alarm rate, while maintaining adequate sensitivity.
Dr. Mitsa’s invaluable expertise and efforts to enlighten the understanding of temporal and spatiotemporal data mining principles, including the latest techniques on temporal pattern discovery, classification, and clustering, have a tremendous impact on a wide array of multidisciplinary areas of science and technology such as biomedicine, defense, business, and industrial applications.
—Dr. George C. Giakos, IEEE Fellow, University of Akron, Ohio, USA

Temporal Data Mining presents a comprehensive overview of the various mathematical and computational aspects of dynamical data processing, from database storage and retrieval to statistical modeling and inference. The first part of the book discusses the key tools and techniques in considerable depth, with a focus on the applicable models and algorithms. Building on this, the second part considers the application to bioinformatics, finance and business computing. The technical depth is appropriate to interest a broad audience, and the text is highly accessible irrespective of the reader’s prior familiarity with the subject. An extensive bibliography is provided on each of the topics covered, which makes this book a valuable reference for both the novice and the established practitioner. The clear, concise and instructive style will make this book particularly attractive to graduate students, researchers and industry professionals.
—Dr. Wasim Q. Malik, Massachusetts Institute of Technology and Harvard Medical School, Cambridge, USA

… how can decision-makers be so data poor in such a (theoretically at least) data-rich economy? Chapter 7 of Theo Mitsa’s book presents the potential for an interesting resolution to this paradox. Her linkage of sophisticated concepts of temporal data mining to practical business issues, such as strategy, forecasting, financial scenario analysis, customer value and retention, operations and logistics management, etc., offers an illuminating approach to organizing and creating sense from overwhelming quantities of random data. Although the algorithms and computations are complex, a reader can learn that there are quantitative approaches to expose additional, possibly critical, insights about virtually any facet of a business. This book further illustrates the growing importance of business analytics and showcases the myriad opportunities available to savvy managers and entrepreneurs to use a system of tools to leverage the value of, and investment in, their data collection and mining efforts.
—Gary Minkoff, Babson MBA, President, Above & Beyond Marketing, Highland Park, New Jersey, USA

As someone who works on signal processing applications in the medical device industry, I found the topic of temporal data mining to be extremely relevant. Our work focuses primarily on time series analysis of evoked potentials. Analysis of these signals is complicated by interfering signals, which although variable, tend to fall into a fairly small number of stereotypical cases. The techniques described in chapter 2 for temporal data similarity calculations and in chapter 3 for temporal data classification have potential application in our work. I found that Temporal Data Mining offered a valuable overview of these fields and gave interesting insight into topics related to gene discovery and bioinformatics. A major strength of the book is the large bibliography, which provides the reader with the tools to dig deeper into topics of interest.
—Dr. Brian Tracey, Signal Processing Project Leader at Neurometrix, Inc.

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