Communications of the ACM
Algorithms for clustering data
Algorithms for clustering data
A deterministic annealing approach to clustering
Pattern Recognition Letters
Training knowledge-based neural networks to recognize genes in DNA sequences
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Self-organizing maps
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
On the 'Dimensionality Curse' and the 'Self-Similarity Blessing'
IEEE Transactions on Knowledge and Data Engineering
Redefining Clustering for High-Dimensional Applications
IEEE Transactions on Knowledge and Data Engineering
Iterative Rank based Methods for Clustering
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
A New Cluster Isolation Criterion Based on Dissimilarity Increments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
Data Mining for Case-Based Reasoning in High-Dimensional Biological Domains
IEEE Transactions on Knowledge and Data Engineering
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
IEEE Transactions on Fuzzy Systems
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Clustering in the membership embedding space
International Journal of Knowledge Engineering and Soft Data Paradigms
A new clustering algorithm for coordinate-free data
Pattern Recognition
Membership embedding space approach and spectral clustering
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
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Clustering algorithms are routinely used in biomedical disciplines, and are a basic tool in bioinformatics. Depending on the task at hand, there are two most popular options, the central partitional techniques and the agglomerative hierarchical clustering techniques and their derivatives. These methods are well studied and well established. However, both categories have some drawbacks related to data dimensionality (for partitional algorithms) and to the bottom-up structure (for hierarchical agglomerative algorithms). To overcome these limitations, motivated by the problem of gene expression analysis with DNA microarrays, we present a hierarchical clustering algorithm based on a completely different principle, which is the analysis of shared farthest neighbors. We present a framework for clustering using ranks and indexes, and introduce the shared farthest neighbors (SFN) clustering criterion. We illustrate the properties of the method and present experimental results on different data sets, using the strategy of evaluating data clustering by extrinsic knowledge given by class labels.