Pattern Theoretic Knowledge Discovery
Abstract
Among the future research directions of Knowledge Discovery in Databases is the ability to extract an overlying concept relating data objects that are useful to the investigator. Some of the current limitations involve the search complexity and what it means to be useful. The Pattern Theory research crosses over in a natural way to the aforementioned domain. The goal of this paper is threefold. First, we wish to present a new approach to the problem of learning by Discovery and robust pattern finding in general. Second, we will show its performance by exhibiting several learning curves. Third, from a practical standpoint, we wish to explore the current limitations of a Pattern Theoretic Discovery and Databases problem. Function decomposition is the central core of Pattern Theory. The development allows us to discuss the notion of patterns, and thus, the notion of useful, in a formal manner. Pattern Theory, Function Decomposition, Machine Learning Patterns, Knowledge Discovery
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1994
- Accession Number
- ADA285472
Entities
People
- Jeffrey A. Goldman
Organizations
- Wright Laboratory