The mining of association rules can provide relevant and novel information to the data analyst. However, current techniques do not take into account that the observed associations may arise from variables that are unrecorded in the database. For instance, the pattern of answers in a large marketing survey might be better explained by a few latent traits of the population than by direct associat...

Most of the machine translation systems rely on a large set of translation rules. These rules are treated as discrete and independent events. In this short paper, we propose a novel method to model rules as observed generation output of a compact hidden model, which leads to better generalization capability. We present a preliminary generative model to test this idea. Experimental results show ...

In this paper, we propose a method of hiding sensitive classification rules from data mining algorithms for categorical datasets. Our approach is to reconstruct a dataset according to the classification rules that have been checked and agreed by the data owner for releasing to data sharing. Unlike the other heuristic modification approaches, firstly, our method classifies a given dataset. Subse...

The problem of maintaining a knowledge base is substantially concerned with keeping track of rules that share common wisdom. A knowledge representation is described in which a collection of rules that are based on common wisdom are represented as a single ‘item’. For items, maintenance hazards, caused by one item being partially hidden within another, still remain. ‘Objects’ are introduced as i...

Association rule mining is concerned with the discovery of interesting association relationships hidden in databases. Existing algorithms typically assume that data characteristics are stable over time. Their main focus is therefore to mine association rules in an efficient manner. However, the world constantly changes. This makes the characteristics of real-life entities represented by the dat...

Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a tendency to be “shared” across several output classes or even may not contribute to any output class. To address this we have developed an algorithm called LREX (for Local Rule EXtraction) which tackles these issues by ex...