Nonlinear pattern hypothesis generation for data mining

摘要

This paper reports on conceptual development in applications of neural networks to data mining and knowledge discovery. Hypothesis generation is one of the significant differences of data mining from statistical analyses. Nonlinear pattern hypothesis generation is a major task of data mining and knowledge discovery. Yet, few methods of nonlinear pattern hypothesis generation are available.This paper proposes a model of data mining to support nonlinear pattern hypothesis generation. This model is an integration of linear regression analysis model, Kohonen's self-organizing maps, the algorithm for convex polytopes, and back-propagation neural networks.