| Evolutionary Computation in Data Mining 2005 Edition Contributor(s): Ghosh, Ashish (Editor) |
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ISBN: 3540223703 ISBN-13: 9783540223702 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: October 2004 Annotation: This carefully edited book reflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications. Evolutionary Computation in Data Mining provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics. |
| Additional Information |
| BISAC Categories: - Mathematics | Applied - Computers | Intelligence (ai) & Semantics - Computers | Databases - General |
| Dewey: 005.74 |
| LCCN: 2004111009 |
| Series: Studies in Fuzziness and Soft Computing |
| Physical Information: 0.69" H x 6.14" W x 9.21" (1.28 lbs) 266 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation). |