Temporal Data Mining

Theophano Mitsa



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.

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