Data Mining and Knowledge Discovery Handbook, 2 Edition part 4. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 10 Oded Maimón and Lior Rokach Full taxonomy - for all the nine steps of the KDD process. We have shown a taxonomy for the DM methods but a taxonomy is needed for each of the nine steps. Such a taxonomy will contain methods appropriate for each step even the first one and for the whole process as well. Meta-algorithms - algorithms that examine the characteristics of the data in order to determine the best methods and parameters including decompositions . Benefit analysis - to understand the effect of the potential KDD DM results on the enterprise. Problem characteristics - analysis of the problem itself for its suitability to the KDD process. Mining complex objects of arbitrary type - Expanding Data Mining inference to include also data from pictures voice video audio etc. This will require adapting and developing new methods for example for comparing pictures using clustering and compression analysis . Temporal aspects - many data mining methods assume that discovered patterns are static. However in practice patterns in the database evolve over time. This poses two important challenges. The first challenge is to detect when concept drift occurs. The second challenge is to keep the patterns up-to-date without inducing the patterns from scratch. Distributed Data Mining - The ability to seamlessly and effectively employ Data Mining methods on databases that are located in various sites. This problem is especially challenging when the data structures are heterogeneous rather than homogeneous. Expanding the knowledge base for the KDD process including not only data but also extraction from known facts to principles for example extracting from a machine its principle and thus being able to apply it in other situations . Expanding Data Mining reasoning to include creative solutions not just the ones that appears in the data but being able to combine solutions and generate another approach. The Organization of the Handbook This handbook is organized in eight parts. .
