Unsupervised Learning of Relational Patterns.

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 the relational pattern space. It starts with general patterns derived from a given database schema, and then iteratively generates new promising patterns using the knowledge dynamically collected in the learning process, such as previously learned patterns. To demonstrate the capability of the algorithm, we show some abstracted database examples, and we also show that some of the well-known examples in the relational concept learning literature can be learned without supervision.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1996
Accession Number
ADA308923

Entities

People

  • Bing Leng
  • Wei-min Shen

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Classification
  • Concept Formation
  • Data Mining
  • Data Science
  • Databases
  • Information Science
  • Learning
  • Literature
  • Machine Learning
  • Relational Databases
  • Standards
  • Supervision
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Linguistics
  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Space