| Title:
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FOUNDATIONS OF COMPUTATIONAL INTELLIGENCE
VOLUME 4: BIO-INSPIRED DATA MINING |
| By: |
Ajith Abraham (Editor), Aboul-Ella Hassanien (Editor), Andre Ponce de Leon F.de Carvalho (Editor) |
| Format: |
Hardback |
| List price:
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£159.99 |
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| ISBN 10: |
3642010873 |
| ISBN 13: |
9783642010873 |
| Publisher: |
SPRINGER-VERLAG BERLIN AND HEIDELBERG GMBH & CO. KG |
| Pub. date: |
15 April, 2009 |
| Edition: |
Softcover reprint of hardcover 1st ed. 2009 |
| Series: |
Studies in Computational Intelligence 204 |
| Pages: |
396 |
| Description: |
Computing techniques inspired by biological elements such as nervous systems, immune systems and genetics have been used in data mining. This book, one of a series on the foundations of Computational Intelligence, is focused on bio-inspired data mining. |
| Synopsis: |
Foundations of Computational Intelligence Volume 4: Bio-Inspired Data Mining Theoretical Foundations and Applications Recent advances in the computing and electronics technology, particularly in sensor devices, databases and distributed systems, are leading to an exponential growth in the amount of data stored in databases. It has been estimated that this amount doubles every 20 years. For some applications, this increase is even steeper. Databases storing DNA sequence, for example, are doubling their size every 10 months. This growth is occurring in several applications areas besides bioinformatics, like financial transactions, government data, environmental mo- toring, satellite and medical images, security data and web. As large organizations recognize the high value of data stored in their databases and the importance of their data collection to support decision-making, there is a clear demand for - phisticated Data Mining tools. Data mining tools play a key role in the extraction of useful knowledge from databases. They can be used either to confirm a parti- lar hypothesis or to automatically find patterns. In the second case, which is - lated to this book, the goal may be either to describe the main patterns present in dataset, what is known as descriptive Data Mining or to find patterns able to p- dict behaviour of specific attributes or features, known as predictive Data Mining. While the first goal is associated with tasks like clustering, summarization and association, the second is found in classification and regression problems. |
| Illustrations: |
58 Tables, black and white; XIV, 396 p. |
| Publication: |
Germany |
| Imprint: |
Springer-Verlag Berlin and Heidelberg GmbH & Co. K |
| Returns: |
Returnable |