Mining Association Rules From Qualitative
And Quantitative Clustering
Author(s)
A. Salazar, J. Gosalbez & I. Bosch
Abstract
A comparison of mining association rules from clusters generated by qualitative
clustering and clusters obtained by quantitative clustering is presented. Whereas
in quantitative clustering only numerical data are included, numerical and
categorical data are used in qualitative clustering for record conglomeration. The
aim of this paper is to compare the performance of the two different kinds of
clustering by analyzing the rules obtained in a real data case from the academic
area. Computational efficiency of the algorithms is compared and the reliability
of the rules has been assessed by experts. For qualitative clustering three
approaches have been applied: Mixed metrics for weighing quantitative and
qualitative variables, symbolic clustering and conjunctive conceptual clustering.
For quantitative clustering the fuzzy c-means algorithm has been applied. All the
clustering methods are arranged in a hierarchical agglomerative scheme, except
the conceptual clustering algorithm. Association rules have been extracted from
the C4.5 decision tree algorithm.
Keywords: qualitative clustering, decision rules, mixed metrics, knowledge
discovery.
1 Introduction
Most of the real applications include qualitative and quantitative variables for
describing its data. The processing of data sets described by mixed or
heterogeneous variables requires a special treatment because the standard
clustering focuses on working with quantitative variables only.
This article presents a study on the use of qualitative or quantitative clustering
in mining association rules. Two possibilities in a data mining process are
Keywords
qualitative clustering, decision rules, mixed metrics, knowledge
discovery.
Related Book
Other papers in this volume
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