Machine learning task as a diclique extracting task

Authors:
Rein Kuusik;Tarvo Treier;Grete Lind;Peeter Roosmann
Affiliations:
Department of Informatics, Tallinn University of Technology, Tallinn, Estonia;Department of Informatics, Tallinn University of Technology, Tallinn, Estonia;Department of Informatics, Tallinn University of Technology, Tallinn, Estonia;Department of Informatics, Tallinn University of Technology, Tallinn, Estonia
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

Citing 7
Cited 0

Experimental comparison of human and machine learning formalisms

Proceedings of the sixth international workshop on Machine learning
The Diclique Representation and Decomposition of Binary Relations

Journal of the ACM (JACM)
Principles of data mining

Principles of data mining
Machine Learning

Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An all closed set finding algorithm for data mining

AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
Algorithm MONSA for all closed sets finding: basic concepts and new pruning techniques

WSEAS Transactions on Information Science and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

As we know there exist several approaches and algorithms for data mining and machine learning task solution, for example, decision tree learning, artificial neural networks, Bayesian learning, instance-based learning, genetic algorithms, etc. They are effective and well-known and their base algorithms and main ideology are published. In this paper we present a new approach for machine learning (ML) task solution, an inductive learning algorithm based on diclique extracting task. We show how to transform ML as inductive leaning task into the graph theoretical diclique extracting task, present an example and discuss about the problems related with that approach and effectiveness of the algorithm.