Mining sequential patterns is an important activity in computerized data analysis, and further analysis of discovered patterns can lead to more important findings in a post mining stage. Methods that integrate the traditional mining tasks with a know

Experience shows that different text classification methods can give different results. We look here at a way of combining the results of two or more different classification methods using an evidential approach. The specific methods we have been experimenting with in our group include the support vector machine, kNN (nearest neighbors), kNN model-based approach (kNNM), and Rocchio methods, but the analysis and methods apply to any methods. We review these learning methods briefly, and then we describe our method for combining the classifiers. In a previous study, we suggested that the combination could be done using evidential operations and that using only two focal points in the mass functions gives good results. However, there are conditions under which we should choose to use more focal points. We assess some aspects of this choice from an reasoning perspective and suggest a refinement of the approach.

In transaction processing, an association is said to exist between two sets of items when a transaction containing one set is likely to also contain the other. In information retrieval, an association between two sets of keywords occurs when they cooccur in a document. Similarly, in data mining, an association occurs when one attribute set occurs together with another. As the number of such associations may be large, maximal association rules are sought, e.g., Feldman et al. (1997, 1998). Rough set theory is a successful tool for data mining. By using this theory, rules similar to maximal associations can be found. However, we show that the rough set approach to discovering knowledge is much simpler than the maximal association method.

Rough set theory is a new mathematical tool to deal with vagueness and uncertainty. To apply the theory, it is important to associate it with efficient and effective computational methods. With a little adjustment, a relation can be used to represent a decision table for use in decision making. By using this kind of table, rough set theory can be applied successfully to rough classification and knowledge discovery. We present computational methods for using rough sets to identify classes in datasets, finding dependencies in relations, and discovering rules which are hidden in databases. The methods are illustrated with a running example from a database of car test results. © 1998 John Wiley & Sons, Inc.

This paper presents a framework for fault diagnosis in distributed expert systems. We investigate computational methods for evidential reasoning in expert systems based on the Guan-Bell approach. Matrices and their products are used to express the reasoning algorithms. This makes the algorithms simpler and perhaps more efficient to implement. To make the description of our method clear, we trace through the analysis of the simplified problem of an example in the context of fault diagnosis in a distributed vehicle monitoring system.>