Enhanced spatiotemporal relational probability trees and forests
Data Mining and Knowledge Discovery
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The real world is composed of sets of objects that move and morph in both space and time. Useful concepts can be defined in terms of the complex interactions between the multi-dimensional attributes of subsets of these objects and of the relationships that exist between them. In this paper, we present Spatiotemporal Multi-dimensional Relational Framework (SMRF) Trees, a new data mining technique that extends the successful Spatiotemporal Relational Probability Tree models. From a set of labeled, multi-object examples of a target concept, our algorithm infers both the set of objects that participate in the concept and the key object and relation attributes that describe the concept. In contrast to other relational model approaches, SMRF trees do not rely on pre-defined relations between objects. Instead, our algorithm infers the relations from the continuous attributes. In addition, our approach explicitly acknowledges the multi-dimensional nature of attributes such as position, orientation and color. Our method performs well in exploratory experiments, demonstrating its viability as a relational learning approach.