Temporal Data Mining: 1st Edition (Hardback) book cover

Temporal Data Mining

1st Edition

By Theophano Mitsa

Chapman and Hall/CRC

395 pages | 31 B/W Illus.

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pub: 2010-03-10
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Description

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/

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.

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.

About the Author

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.

About the Series

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM021030
COMPUTERS / Database Management / Data Mining
COM051300
COMPUTERS / Programming / Algorithms
MAT029000
MATHEMATICS / Probability & Statistics / General