An efficient clustering algorithm for mining fuzzy quantitative association rules

Mining association rules on categorical data has been discussed widely. It is a relatively difficult problem in the discovery of association rules from numerical data, since the reasonable intervals for unknown numerical attributes or quantitative data may not be discriminated easily. We propose an efficient hierarchical clustering algorithm based on variation of density to solve the problem of interval partition. We define two main characteristics of clustering numerical data: relative inter-connectivity and relative closeness. By giving a proper parameter, α, to determine the importance between relative closeness and relative inter-connectivity, the proposed approach can generate a reasonable interval automatically for the user. The experimental results show that the proposed clustering algorithm can have good performance on both clustering results and speed