Intelligent Data Analysis: An International Journal

Classification, which involves finding rules that partition a given dataset into disjoint groups, is one class of data mining problems. Approaches proposed so far for mining classification rules from databases are mainly decision tree based on symbolic learning methods. In this paper, we combine artificial neural network and genetic algorithm to mine classification rules. Some experiments have demonstrated that our method generates rules of better performance than the decision tree approach and the number of extracted rules is fewer than that of C4.5.

If it is to qualify as knowledge, a learner's output should be accurate, stable and comprehensible. Learning multiple models can improve significantly on the accuracy and stability of single models, but at the cost of losing their comprehensibility (when they possess it, as do, for example, simple decision trees and rule sets). This article proposes and evaluates CMM, a meta-learner that seeks to retain most of the accuracy gains of multiple model approaches, while still producing a single comprehensible model. CMM is based on reapplying the base learner to recover the frontiers implicit in the multiple model ensemble. This is done by giving the base learner a new training set, composed of a large number of examples generated and classified according to the ensemble, plus the original examples. CMM is evaluated using C4.5RULES as the base learner, and bagging as the multiple-model methodology. On 26 benchmark datasets, CMM retains on average 60% of the accuracy gains obtained by bagging relative to a single run of C4.5RULES, while producing a rule set whose complexity is typically a small multiple (2–6) of C4.5RULES's, and also improving stability. Further studies show that accuracy and complexity can be traded off by varying the number of artificial examples generated.

A fast and simple algorithm for approximately calculating the principal components (PCs) of a dataset and so reducing its dimensionality is described. This Simple Principal Components Analysis (SPCA) method was used for dimensionality reduction of two high-dimensional image databases, one of handwritten digits and one of handwritten Japanese characters. It was tested and compared with other techniques. On both databases SPCA shows a fast convergence rate compared with other methods and robustness to the reordering of the samples.

One of the most important problems on rule induction methods is that they cannot extract rules, which plausibly represent experts' decision processes. On one hand, rule induction methods induce probabilistic rules, the description length of which is too short, compared with the experts' rules. On the other hand, construction of Bayesian networks generates too lengthy rules. In this paper, the characteristics of experts' rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several groups with respect to the characterization. Then, two kinds of sub-rules, characterization rules for each group and discrimination rules for each class in the group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on medical databases, the experimental results of which show that induced rules correctly represent experts' decision processes.

Automatic clustering methods are part of data mining methods. They aim at building clusters of items so that similar items fall into the same cluster while unsimilar items fall into separate clusters. A particular class of clustering methods are hierarchical ones where recursive clusters are formed to grow a binary tree representing an approximation of similarities between items. We propose a new interactive interface to help the user to interpret the result of such a clustering process, according to the item characteristics. The prototype has been applied successfully to a special case of items providing nice graphical representations (electric load curves) but can also be used with other types of curves or with more standard items.

We briefly review each paper of the Fourteenth International Conference on Machine Learning, along with some general observations on the conference as a whole. The major topics of papers include data reduction, feature selection, ensembles of classifiers, natural language learning, text categorization, inductive logic programming, stochastic models, and reinforcement learning.