Journal article
Graph-based relational learning: current and future directions
SIGKDD explorations, Vol.5(1), pp.90-93
07/2003
Handle:
https://hdl.handle.net/2376/109131
Abstract
Graph-based relational learning (GBRL) differs from logic-based relational learning, as addressed by inductive logic programming techniques, and differs from frequent subgraph discovery, as addressed by many graph-based data mining techniques. Learning from graphs, rather than logic, presents representational issues both in input data preparation and output pattern language. While a form of graph-based data mining, GBRL focuses on identifying novel, not necessarily most frequent, patterns in a graph-theoretic representation of data. This approach to graph-based data mining provides both simplifications and challenges over frequency-based approaches. In this paper we discuss these issues and future directions of graph-based relational learning.
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Details
- Title
- Graph-based relational learning
- Creators
- Lawrence B Holder - University of Texas at Arlington, Arlington, TXDiane J Cook - University of Texas at Arlington, Arlington, TX
- Publication Details
- SIGKDD explorations, Vol.5(1), pp.90-93
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Identifiers
- 99900547516801842
- Language
- English
- Resource Type
- Journal article