Kumar P., Krishna P.R., Raju S.B. (Eds.) Pattern Discovery Using Sequence Data Mining: Applications and Studies

IGI Global, 2012. – 286 p. – ISBN: 1613500564, 9781613500569A huge amount of data is collected every day in the form of sequences. These sequential data are valuable sources of information not only to search for a particular value or event at a specific time, but also to analyze the frequency of certain events or sets of events related by particular temporal/sequential relationship. For example, DNA sequences encode the genetic makeup of humans and all other species, and protein sequences describe the amino acid composition of proteins and encode the structure and function of proteins. Moreover, sequences can be used to capture how individual humans behave through various temporal activity histories such as weblog histories and customer purchase patterns. In general there are various methods to extract information and patterns from databases, such as time series approaches, association rule mining, and data mining techniques.The objective of this book is to provide a concise state-of-the-art in the field of sequence data mining along with applications. The book consists of 14 chapters divided into 3 sections. The first section provides review of state-of-art in the field of sequence data mining. Section 2 presents relatively new techniques for sequence data mining. Finally, in section 3, various application areas of sequence data mining have been explored.Applications of Pattern Discovery Using Sequential Data Mining
A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part I: Sequences of Continuous Feature Vectors
A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part II: Sequences of Discrete Symbols
Mining Statistically Significant Substrings Based on the Chi-Square Measure
Unbalanced Sequential Data Classification using Extreme Outlier Elimination and Sampling Techniques
Quantization based Sequence Generation and Subsequence Pruning for Data Mining Applications
Classification of Biological Sequences
Approaches for Pattern Discovery Using Sequential Data Mining
Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques
Identification of Genomic Islands by Pattern Discovery
Video Stream Mining for On-Road Traffic Density Analytics
Discovering Patterns in Order to Detect Weak Signals and Define New Strategies
Discovering Patterns for Architecture Simulation by Using Sequence Mining
Sequence Pattern Mining for Web Logs