BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A robust and scalable clustering algorithm for mixed type attributes in large database environment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
INCONCO: interpretable clustering of numerical and categorical objects
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Integrative parameter-free clustering of data with mixed type attributes
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Dependency clustering across measurement scales
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Clustering is a widely used technique in data mining, now there exists many clustering algorithms, but most existing clustering algorithms either are limited to handle the single attribute or can handle both data types but are not efficient when clustering large data sets. Few algorithms can do both well. In this paper, we propose a clustering algorithm CFIKP that can handle large datasets with mixed type of attributes. We first use CF*-tree to pre-cluster datasets. After the dense regions are stored in leaf nodes, then we look every dense region as a single point and use an improved k-prototype to cluster such dense regions. Experiments show that the CFIKP algorithm is very efficient in clustering large datasets with mixed type of attributes.