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Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
(Institute of Electrical and Electronics Engineers Inc., 2018)
In Pattern Recognition there are many algorithms it try to solve the problem of grouping objects of the same type, this is called clustering, however the task of dividing these lies not only in the objective function, but ...
GibbsCluster: unsupervised clustering and alignment of peptide sequences
(Oxford University Press, 2017-04)
Receptor interactions with short linear peptide fragments (ligands) are at the base of many biological signaling processes. Conserved and information-rich amino acid patterns, commonly called sequence motifs, shape and ...
Interpretable clustering using unsupervised binary trees
(Springer, 2013-03)
We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity ...
Unsupervised Feature Selection Methodology for Clustering in High Dimensionality Datasets
(Instituto de Informática - Universidade Federal do Rio Grande do Sul, 2020)
ClusMAM: fast and effective unsupervised clustering of large complex datasets using metric access methods
(Association for Computing Machinery - ACMUniversity of PisaScuola Superiore Sant’AnnaPisa, 2016-04)
An efficient and effective clustering process is a core task of data mining analysis, and has become more important in the nowadays scenario of big data, where scalability is an issue. In this paper we present the ClusMAM ...
Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach
(F1000 Research, 2020)
Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access ...
An Automatic Merge Technique to Improve the Clustering Quality Performed by LAMDA
(Institute of Electrical and Electronics Engineers Inc., 2020-01-01)
Clustering is a research challenge focused on discovering knowledge from data samples whose goal is to build good quality partitions. In this paper is proposed an approach based on LAMDA (Learning Algorithm for Multivariable ...
Hierarchical density estimates for data clustering, visualization, and outlier detection
(ACMNew York, 2015-07)
An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level ...
Multispectral images segmentation using new fuzzy cluster centroid modified
(Institute of Electrical and Electronics Engineers Inc., 2017)
The presence of outliers, noise, corrupt pieces of data and great quantity of samples in a multispectral image, makes the segmentation analysis work tedious. The fuzzy clustering approach, specially, is susceptible to ...
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach
(2021-01-01)
The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, ...