Clustering Algorithms for Huge Datasets: A Mathematical Approach
نویسندگان
چکیده
منابع مشابه
Efficient algorithms for exact hierarchical clustering of huge datasets: Tackling the entire protein space
Motivation: UPGMA (average-linkage clustering) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. UPGMA however, is a complete-linkage method, in the sense that all edges between data points are needed in memory. Due to this prohibitive memory requirement UPGMA is not scalable for very large datasets. Results: We present novel memory-co...
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MOTIVATION UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets. APPLICATION We present a novel class of memory-constrained UPGMA (MC-UPGMA) algorithms. Given any pr...
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Clustering algorithms are employed in many bioinformatics tasks, including categorization of protein sequences and analysis of gene-expression data. Although these algorithms are routinely applied, many of them suffer from the following limitations: (i) relying on predetermined parameters tuning, such as a-priori knowledge regarding the number of clusters; (ii) involving nondeterministic proced...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2019
ISSN: 0975-8887
DOI: 10.5120/ijca2019918724