Logic Mining Using Neural Networks

Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Data mining methods are important in the management of complex systems. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. Neural networks have been successfully applied in wide range of supervised and unsupervised learning applications. Neural network methods are not commonly used for data mining tasks, because they often produce incomprehensible models, and require long training times. One way in which the collective properties of a neural network may be used to implement a computational task is by way of the concept of energy minimization. The Hopfield network is well-known example of such an approach. The Hopfield network is useful as content addressable memory or an analog computer for solving combinatorial-type optimization problems. Wan Abdullah [1] proposed a method of doing logic programming on a Hopfield neural network. Optimization of logical inconsistency is carried out by the network after the connection strengths are defined from the logic program; the network relaxes to neural states corresponding to a valid interpretation. In this article, we describe how Hopfield network is able to induce logical rules from large database by using reverse analysis method: given the values of the connections of a network, we can hope to know what logical rules are entrenched in the database. Key-words: Hopfield, Logic Programming, data mining, neural network 1.0 Introduction The main focus of the data mining task is to gain insight into large collections of data. Often achieving this goal involves applying machinelearning methods to inductively construct models of the data at hand. Although neural network learning algorithms have been successfully applied in wide range of supervised and unsupervised learning applications, they have not often been applied in data mining settings, in which two fundamental considerations are the comprehensibility and speed issues which often are of prime importance in the data mining community. Data mining is not merely automatic collecting of knowledge . Human-computer collaboration knowledge discovery is the interactive process between data miner and and computer. The aim is to ext ract novel, plausible, relevant and interesting knowledge from the database. We do not provide an introduction to data mining techniques in this paper, but instead refer the interested reader to one of the good book in the field [ 2 ]. Logic programming can be treated as a problem in combinatorial optimization. Therefore it can be carried out in a neural network to obtain the desired solution. Our objective is to find a set of interpretation (i.e., truth value assignments) for the atoms in the clauses which satisfy the clauses (which yields all the clauses true). We extended the work related to logic programming in neural network by introducing reverse analysis method. This method is capable to induce logical rules entrenched in a database. The knowledge obtained from the logical rules can be used to unearth relationship in data that may provide useful insights. The rest of the paper organized as follows. In the next section we consider some theory of the Proceedings of the International Conference on Intelligent Systems 2005 (ICIS 2005) Kuala Lumpur, 1 – 3 December 2005