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 categori cal data clustering. especially for transactional data characteri propellant by high dimensionality and large volume. St. arting from a heuristic method of increasing th e height-to-width ratio of the cluster histogr am, we develop a novel algorithm-CLOPE. which is very fast and scalable, while being quite effective. We demonstrate th e performance of our algorithm on two real world