Description: 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 Platform: |
Size: 108363 |
Author:hanzhang |
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Description: 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 Platform: |
Size: 108544 |
Author:hanzhang |
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Description: 实现了两种基于粗糙集模型的层次聚类算法，采用java编程语言实现(Hierarchical clustering algorithm for categorical data using
a probabilistic rough set model) Platform: |
Size: 957440 |
Author:迷糊的胡胡 |
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Search - cATEGORICAL CLUSTERING