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Abstract: Learning relational patterns without supervision is a challenging and open problem for machine learning, yet a very desirable feature for real-world applications. In this paper, we present a new algorithm for unsupervised learning of relational patterns. Given a relational database, this algorithm can find function-free Horn clauses without requiring users to label the data as positive or negative examples, and specify what target concepts to learn. The algorithm is a heuristic search through... (Update)
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BibTeX entry: (Update)
@misc{ ns-unsupervised,
author = "Er Ns",
title = "Unsupervised Learning of Relational Patterns",
url = "citeseer.ist.psu.edu/33784.html" }
Citations (may not include all citations):1450 The Design and Analysis of Computer Algorithms (context) - Aho, Hopcroft et al. - 1974
492 Learning Logical Definitions from Relations (context) - Quinlan - 1990
154 Machine invention of first order predicates by inverting res.. (context) - Muggleton, Buntine - 1988
130 Inverse entailment and Progol - Muggleton - 1995
117 The Utility of Knowledge in Inductive Learning - Pazzani, Kibler - 1992
92 Learning distributed representations of concepts (context) - Hinton - 1986
47 A Polynomial Approach to the Constructive Induction of Struc.. (context) - Kietz, Morik - 1994
41 Metaqueries for Data Mining - Shen, Ong et al. - 1995
35 Conceptual clustering in a first order logic representation - Bisson - 1992
16 Discovering regularities from Knowledge Bases - Shen - 1992
11 Small is Beautiful: A Brute-Force Approach to Learning First.. - Minton, Underwood - 1994
9 Incremental concept formation with composite objects (context) - Thompson, Langley - 1989
4 KBG, A Knowledge Based Generalizer (context) - Bisson - 1990
2 Inventing Goal-Oriented Classifications of Structured Object.. - Stepp, Michalski - 1986
2 Concept Formation During Interactive Theory Revision (context) - Wrobe - 1994
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