Bøker av Sumeet Dua

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field. The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections:Supplies a complete overview of the evolution of the field and its intersection with computational learningDescribes the role of data mining in analyzing large biological databasesΓÇöexplaining the breath of the various feature selection and feature extraction techniques that data mining has to offerFocuses on concepts of unsupervised learning using clustering techniques and its application to large biological dataCovers supervised learning using classification techniques most commonly used in bioinformaticsΓÇöaddressing the need for validation and benchmarking of inferences derived using either clustering or classificationThe book describes the various biological databases prominently referred to in bioinformatics and includes a detailed list of the applications of advanced clustering algorithms used in bioinformatics. Highlighting the challenges encountered during the application of classification on biologica

Covering theory, algorithms, and methodologies, as well as data mining technologies, this book presents a discussion of data-intensive computations used in data mining applied to bioinformatics. It explains data mining design concepts to build applications and systems.

"This interdisciplinary assessment is especially useful for students, who typically learn cybersecurity, machine learning, and data mining in independent courses. Machine learning and data mining play significant roles in cybersecurity, especially as more challenges appear with the rapid development of information discovery techniques, such as those originating from the sheer dimensionality and heterogeneous nature of the network data, the dynamic change of threats, and the severe imbalanced classes of normal and anomalous behaviors"--