Machine Learning Task as a Diclique Extracting Task

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.