Learning rules from numerical data by combining geometric and graph-theoretic approach
Document Type
Article
Publication Date
11-30-2000
Abstract
We present a new method for learning rules from numerical data by using a combination of geometric and graph-theoretic methods. Three different graphs are defined to capture the geometric properties of the data-set. The graphs G (P) and G (N) capture the intra-class properties of the set of positive points P and the set of negative points N, respectively, and the graph G(P, N) captures the inter-class properties between P and N. We derive rules from these graphs by means of graph partitioning. Our method tends to give fewer rules than that obtained by decision-tree based methods. The complexity of our algorithm is O(M3), where M = |P|+|N|.
Publication Source (Journal or Book title)
Robotics and Autonomous Systems
First Page
135
Last Page
147
Recommended Citation
Kundu, S., & Chen, J. (2000). Learning rules from numerical data by combining geometric and graph-theoretic approach. Robotics and Autonomous Systems, 33 (2), 135-147. https://doi.org/10.1016/S0921-8890(00)00084-1