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Data Mining and Knowledge Discovery Handbook, 2 Edition part 119

Data Mining and Knowledge Discovery Handbook, 2 Edition part 119. 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. | 1160 Boris Kovalerchuk and Evgenii Vityaev ment that Data Mining is now very much an art and to make it into a science we need more work in areas like ILP that is a part of relational learning that includes probabilistic learning. 60.3.3 Problem ID and method profile Selection of a method for discovering regularities in financial time series is a very complex task. Uncertainty of problem descriptions and method capabilities are among the most obvious difficulties in this process. Dhar and Stein 1997 introduced and applied a unified vocabulary for business computational intelligence problems and methods that provide a framework for matching problems and methods. A problem is described using a set of desirable values problem ID profile and a method is described using its capabilities in the same terms. Use of unified terms dimensions for problems and methods enhances capabilities of comparing alternative methods. Introducing dimensions also accelerates their clarification. Next users should not be forced to spend time determining a method s capabilities values of dimensions for the method . This is a task for developers but users should be able to identify desirable values of dimensions using natural language terms as suggested by Dhar and Stein 1997 . Along these lines Table 60.1 indicates three shortcomings of neural networks for stock price forecasting related to explainability usage of logical relations and tolerance for sparse data. The strength of neural networks is also indicated by lines where requested capabilities are satisfied by neural networks. The advantages of using neural network models include the ability to model highly complex functions and to use a high number of variables including both fundamental and technical factors. Table 60.1. Comparison of model quality and resources Dimension Desirable value for stock price forecast problem Capability of a neural network method Accuracy Moderate High Explainability Moderate to High Low to Moderate .