Algorithms for clustering data
Algorithms for clustering data
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
ACM Computing Surveys (CSUR)
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
MDL-Based Selection of the Number of Components in Mixture Models for Pattern Classification
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A New Cluster Isolation Criterion Based on Dissimilarity Increments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
EBEM: An Entropy-based EM Algorithm for Gaussian Mixture Models
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
SMEM Algorithm for Mixture Models
Neural Computation
In search of deterministic methods for initializing K-means and Gaussian mixture clustering
Intelligent Data Analysis
NIST Handbook of Mathematical Functions
NIST Handbook of Mathematical Functions
On the distribution of dissimilarity increments
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
Survey of clustering algorithms
IEEE Transactions on Neural Networks
k-nearest neighbor classification using dissimilarity increments
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Image annotation using high order statistics in non-Euclidean spaces
Journal of Visual Communication and Image Representation
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This paper addresses the use of high order dissimilarity models in data mining problems. We explore dissimilarities between triplets of nearest neighbors, called dissimilarity increments (DIs). We derive a statistical model of DIs for d-dimensional data (d-DID) assuming that the objects follow a multivariate Gaussian distribution. Empirical evidence shows that the d-DID is well approximated by the particular case d=2. We propose the application of this model in clustering, with a partitional algorithm that uses a merge strategy on Gaussian components. Experimental results, in synthetic and real datasets, show that clustering algorithms using DID usually outperform well known clustering algorithms.