BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Clustering Algorithms
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
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A density-based clustering algorithm, called OUTCLUST, is presented. The algorithm exploits a notion of local density in order to find homogeneous groups of objects as opposite to objects mostly deviating from the overall population. The proposed algorithm tries to simultaneously consider several features of real data sets, namely finding clusters of different shapes and densities in high dimensional data in presence of noise. It is shown that the method is able to identify very meaningful clusters, and experimental comparison with partitioning, hierarchial, and density-based clustering algorithms, is presented, pointing out that the algorithm achieves good clustering quality.