scholarly journals Cluster algorithms

2008 ◽  
pp. 36-49 ◽  
Author(s):  
Ferenc Niedermayer
Keyword(s):  
Cluster Algorithms

scholarly journals Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature

2021 ◽  
Vol 1 ◽  
pp. 100010
Author(s):  
Sandeep Reddy ◽  
Ravi Bhaskar ◽  
Sandosh Padmanabhan ◽  
Karin Verspoor ◽  
Chaitanya Mamillapalli ◽  
...  
Keyword(s):  
Text Mining ◽  
Medical Literature ◽  
Virus Disease ◽  
Cluster Algorithms ◽  
Corona Virus

PARALLEL ACCELERATION ALGORITHM FOR SPIN MODELS

1992 ◽  
Vol 03 (04) ◽  
pp. 605-610 ◽  
Author(s):  
G.V. BHANOT ◽  
S.L. ADLER
Keyword(s):  
Massively Parallel ◽  
Xy Model ◽  
Spin Models ◽  
Cluster Algorithms ◽  
Multi Scale ◽  
Connection Machine ◽  
Parallel Supercomputer ◽  
Parallel Acceleration

We describe and implement a multi-scale acceleration algorithm for spin models on a massively parallel supercomputer, the Connection Machine CM-200. Unlike usual cluster algorithms, our algorithm is completely parallelizable. The time to update all variables in a system of volume Ld scales as Ld log 2L. We prove this by computing the time for one lattice sweep for the 2-d XY model for our algorithm on lattices of size up to 2048×2048.

Separating Phages From Other Virus Families and Classifying the Different Phage Families By GI-Clusters

2021 ◽  
Author(s):  
Xingang Jia ◽  
Qiuhong Han ◽  
Zuhong Lu
Keyword(s):  
Test Data ◽  
Cluster Algorithm ◽  
Nearest Neighbors ◽  
Euclidean Algorithm ◽  
Training Data ◽  
Data Sets ◽  
Maximum Element ◽  
Clustering Techniques ◽  
Cluster Algorithms ◽  
Biological Entities

Abstract Background: Phages are the most abundant biological entities, but the commonly used clustering techniques are difficult to separate them from other virus families and classify the different phage families together.Results: This work uses GI-clusters to separate phages from other virus families and classify the different phage families, where GI-clusters are constructed by GI-features, GI-features are constructed by the togetherness with F-features, training data, MG-Euclidean and Icc-cluster algorithms, F-features are the frequencies of multiple-nucleotides that are generated from genomes of viruses, MG-Euclidean algorithm is able to put the nearest neighbors in the same mini-groups, and Icc-cluster algorithm put the distant samples to the different mini-clusters. For these viruses that the maximum element of their GI-features are in the same locations, they are put to the same GI-clusters, where the families of viruses in test data are identified by GI-clusters, and the families of GI-clusters are defined by viruses of training data.Conclusions: From analysis of 4 data sets that are constructed by the different family viruses, we demonstrate that GI-clusters are able to separate phages from other virus families, correctly classify the different phage families, and correctly predict the families of these unknown phages also.

Dunn’s index for cluster tendency assessment of pharmacological data sets

2012 ◽  
Vol 90 (4) ◽  
pp. 425-433 ◽  
Author(s):  
Oscar Miguel Rivera-Borroto ◽  
Mónica Rabassa-Gutiérrez ◽  
Ricardo del Corazón Grau-Ábalo ◽  
Yovani Marrero-Ponce ◽  
José Manuel García-de la Vega
Keyword(s):  
Classification Accuracy ◽  
Molecular Descriptors ◽  
Visual Assessment ◽  
Data Sets ◽  
Single Linkage ◽  
Cluster Algorithms ◽  
Pharmacological Data ◽  
Cluster Separability ◽  
Intensity Image ◽  
Weak Tendency

Cluster tendency assessment is an important stage in cluster analysis. In this sense, a group of promising techniques named visual assessment of tendency (VAT) has emerged in the literature. The presence of clusters can be detected easily through the direct observation of a dark blocks structure along the main diagonal of the intensity image. Alternatively, if the Dunn’s index for a single linkage partition is greater than 1, then it is a good indication of the blocklike structure. In this report, the Dunn’s index is applied as a novel measure of tendency on 8 pharmacological data sets, represented by machine-learning-selected molecular descriptors. In all cases, observed values are less than 1, thus indicating a weak tendency for data to form compact clusters. Other results suggest that there is an increasing relationship between the Dunn’s index as a measure of cluster separability and the classification accuracy of various cluster algorithms tested on the same data sets.

Effects of flipping rules in cluster algorithms

1993 ◽  
Vol 47 (4) ◽  
pp. R1285-R1289 ◽  
Author(s):  
Werner Kerler
Keyword(s):  
Cluster Algorithms

Possibilistic Clustering Methods for Interval-Valued Data

2014 ◽  
Vol 22 (02) ◽  
pp. 263-291 ◽  
Author(s):  
Bruno Almeida Pimentel ◽  
Renata M. C. R. De Souza
Keyword(s):  
Credit Card ◽  
Data Sets ◽  
Clustering Methods ◽  
Cluster Algorithms ◽  
Research Areas ◽  
Possibilistic Clustering ◽  
Membership Value ◽  
Type Data ◽  
Interval Type ◽  
Better Than

Outliers may have many anomalous causes, for example, credit card fraud, cyberintrusion or breakdown of a system. Several research areas and application domains have investigated this problem. The popular fuzzy c-means algorithm is sensitive to noise and outlying data. In contrast, the possibilistic partitioning methods are used to solve these problems and other ones. The goal of this paper is to introduce cluster algorithms for partitioning a set of symbolic interval-type data using the possibilistic approach. In addition, a new way of measuring the membership value, according to each feature, is proposed. Experiments with artificial and real symbolic interval-type data sets are used to evaluate the methods. The results of the proposed methods are better than the traditional soft clustering ones.

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