New Methods for Mining Sequential and Time Series Data

Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. This book defines key concepts in the field of data mining, and presents an overview of previous work in the areas of spatial, spatio-temporal and time series mining. In addition to that, the book provides details about three new data mining techniques, which have been developed to extract specific type of patterns within the large sequential and time series data. If you are interested in databases, and have not been following the data mining field, this book is a great way to catch up on this exciting progress more specifically in the sequential and time series domains.

Ghazi Al-Naymat, PhD: Data Mining and Knowledge Discovery. The University of Sydney, Australia. Research interests include: Machine learning and Data Mining.