BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth 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
An efficient approach for the rank aggregation problem
Theoretical Computer Science
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the closest string via rank distance
CPM'12 Proceedings of the 23rd Annual conference on Combinatorial Pattern Matching
A Low-complexity Distance for DNA Strings
Fundamenta Informaticae
On the Classification and Aggregation of Hierarchies with ifferent Constitutive Elements
Fundamenta Informaticae
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This paper aims to present two clustering methods based on rank distance. The K-means algorithm represents each cluster by a single mean vector. The mean vector is computed with respect to a distance measure. A new K-means algorithm based on rank distance is described in this paper. Hierarchical clustering builds models based on distance connectivity. Our paper introduces a new hierarchical clustering technique that uses rank distance. Experiments using mitochondrial DNA sequences extracted from several mammals demonstrate the clustering performance and the utility of the two algorithms.