This paper studies the problem of categorical data clustering,
especially for transactional data ch - 资源详细说明
This paper studies the problem of categorical data clustering,
especially for transactional data characterized by high
dimensionality and large volume. Starting from a heuristic method
of increasing the height-to-width ratio of the cluster histogram, we
develop a novel algorithm – CLOPE, which is very fast and
scalable, while being quite effective. We demonstrate the
performance of our algorithm on two real world
This paper studies the problem of categorical data clustering,
especially for transactional data ch - 源码文件列表