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For example, clustering of sequences from commercial data sets may help marketer identify different customer groups based upon their purchasing patterns.
The key idea of our approach is to find a set of features that capture the sequential nature of the various data-sequences, project each data-sequence into a ...
In this paper we present an entirely different approach to sequence clustering that does not require an all-against-all analysis and uses a near- linear ...
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Density Peaks Clustering (DPC) algorithm is a kind of density-based clustering approach, which can quickly search and find density peaks. However, DPC has ...
Over the years, many methods have been developed for clustering objects according to their similarity. However these methods tend to have a computational ...
For example, clustering of sequences from commercial data sets may help marketer identify different customer groups based upon their purchasing patterns.
Department of Computer Science and Engineering. University of Minnesota. 4-192 EECS Building. 200 Union Street SE. Minneapolis, MN 55455-0159 USA. TR 01-032.
One of the key steps in all clustering algorithms is the method used to compute the similarity between the objects being clustered. In the context of protein ...
Oct 22, 2024 · However, few clustering algorithms consider sequentiality. In this paper, we study how to cluster sequence datasets. We propose a new similarity ...
Dec 21, 2016 · Here, we report a scalable Dirichlet Process Means (DP-means) algorithm for clustering extremely large sequencing data, termed DACE. With an ...