Research on NMF Based Hierarchical Clustering Methods

2010 ◽  
Vol 439-440 ◽  
pp. 1306-1311
Author(s):  
Fang Li ◽  
Qun Xiong Zhu
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Clustering Method ◽  
Hierarchical Agglomerative Clustering ◽  
Hierarchical Clustering Methods

LSI based hierarchical agglomerative clustering algorithm is studied. Aiming to the problems of LSI based hierarchical agglomerative clustering method, NMF based hierarchical clustering method is proposed and analyzed. Two ways of implementing NMF based method are introduced. Finally the result of two groups of experiment based on the TanCorp document corpora show that the method proposed is effective.

scholarly journals The Effect of Clustering in Filter Method Results Applied in Medical Datasets

2021 ◽  
Vol 16 (1) ◽  
pp. 38-57
Author(s):  
Nadjla Elong ◽  
Sidi Ahmed Rahal
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Hybrid Approach ◽  
Filter Method ◽  
Agglomerative Clustering ◽  
Clustering Method ◽  
Formal Evaluation ◽  
Filter Methods ◽  
Hierarchical Agglomerative Clustering ◽  
Analytic Processing

For a deeper and richer analytic processing of medical datasets, feature selection aims to eliminate redundant and irrelevant features from the data. While filter has been touted as one of the simplest methods for feature selection, its applications have generally failed to identify and deal with embedded similarities among features. In this research, a hybrid approach for feature selection based on combining the filter method with the hierarchical agglomerative clustering method is proposed to eliminate irrelevant and redundant features in four medical datasets. A formal evaluation of the proposed approach unveils major improvements in the classification accuracy when results are compared to those obtained via only the applications of the filter methods and/or more classical-based feature selection approaches.

scholarly journals Clustering Techniques for Secondary Substations Siting

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1028
Author(s):  
Silvia Corigliano ◽  
Federico Rosato ◽  
Carla Ortiz Dominguez ◽  
Marco Merlo
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Universal Access ◽  
Distribution Networks ◽  
Industrialized Countries ◽  
Agglomerative Clustering ◽  
Clustering Techniques ◽  
Hierarchical Agglomerative Clustering ◽  
Efficient Planning ◽  
Target Set

The scientific community is active in developing new models and methods to help reach the ambitious target set by UN SDGs7: universal access to electricity by 2030. Efficient planning of distribution networks is a complex and multivariate task, which is usually split into multiple subproblems to reduce the number of variables. The present work addresses the problem of optimal secondary substation siting, by means of different clustering techniques. In contrast with the majority of approaches found in the literature, which are devoted to the planning of MV grids in already electrified urban areas, this work focuses on greenfield planning in rural areas. K-means algorithm, hierarchical agglomerative clustering, and a method based on optimal weighted tree partitioning are adapted to the problem and run on two real case studies, with different population densities. The algorithms are compared in terms of different indicators useful to assess the feasibility of the solutions found. The algorithms have proven to be effective in addressing some of the crucial aspects of substations siting and to constitute relevant improvements to the classic K-means approach found in the literature. However, it is found that it is very challenging to conjugate an acceptable geographical span of the area served by a single substation with a substation power high enough to justify the installation when the load density is very low. In other words, well known standards adopted in industrialized countries do not fit with developing countries’ requirements.

scholarly journals Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chang Su ◽  
Zhenxing Xu ◽  
Katherine Hoffman ◽  
Parag Goyal ◽  
Monika M. Safford ◽  
...  
Keyword(s):  
New York ◽  
Respiratory Failure ◽  
Sofa Score ◽  
Severity Of Illness ◽  
Agglomerative Clustering ◽  
Baseline Severity ◽  
Organ Systems ◽  
Hierarchical Agglomerative Clustering ◽  
Dynamic Time ◽  
Post Intubation

AbstractCOVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Understanding whether there are distinct subphenotypes of severe COVID-19 may offer insight into its pathophysiology. Sequential Organ Failure Assessment (SOFA) score is an objective and comprehensive measurement that measures dysfunction severity of six organ systems, i.e., cardiovascular, central nervous system, coagulation, liver, renal, and respiration. Our aim was to identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of SOFA score. Intubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Distinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p = 0.033; intermediate stratum, 29.3% vs. 8.0%, p = 0.002; severe stratum, 53.7% vs. 22.2%, p < 0.001). Pathophysiologic biomarkers associated with progression were distinct at each stratum, including findings suggestive of inflammation in low baseline severity of illness versus hemophagocytic lymphohistiocytosis in higher baseline severity of illness. The findings suggest that there are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Distinct progression biomarkers at differential baseline severity of illness suggests a heterogeneous pathobiology in the progression of COVID-19 respiratory failure.

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