Consensus-Driven Cluster Analysis: Top-Down and Bottom-Up Based Split-and-Merge Classifiers

2017 ◽  
Vol 26 (04) ◽  
pp. 1750018
Author(s):  
Mohamed Ali Zoghlami ◽  
Minyar Sassi Hidri ◽  
Rahma Ben Ayed
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Synthetic Data ◽  
Stochastic Methods ◽  
Consensus Clustering ◽  
Top Down ◽  
Bottom Up ◽  
Numerical Examples ◽  
Split And Merge ◽  
Consensus Partition

Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Typically, the goal is searching for the socalled median (or consensus) partition, i.e. the partition that is most similar, on average, to all the input partitions. In this paper we address the problem of combining multiple fuzzy clusterings without access to the underlying features of the data while basing on inter-clusters similarity. We are concerned of top-down and bottom-up based consensus-driven fuzzy clustering while splitting and merging worst clusters. The objective is to reconcile a structure, developed for patterns in some dataset with the structural findings already available for other related ones. The proposed classifiers consider dispersion and dissimilarity between the partitions as well as the corresponding fuzzy proximity matrices. Several illustrative numerical examples, using both synthetic data and those coming from available machine learning repositories, are also included. The experimental component of the study shows the efficiency of the proposed classifiers in terms of quality and runtime.

Top-Down vs Bottom-Up Approaches to User Segmentation: The Best of Both Worlds

2017 ◽  
Vol 61 (1) ◽  
pp. 515-519
Author(s):  
Stefania Mereu ◽  
Matt Newman ◽  
Michelle Peterson ◽  
Eric Taylor ◽  
Jessica White-Sustaita ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Data Gathering ◽  
Design Teams ◽  
Top Down ◽  
Data Set ◽  
Bottom Up ◽  
User Behaviors ◽  
Statistical Cluster ◽  
Rich Data ◽  
Agile Software

Within the fast-paced world of Lean and Agile software development, researchers are always on the lookout for methods that allow for rapid data gathering and analysis, while still yielding robust design recommendations. This paper considers the use cases for “top-down” hypothesis testing and “bottom-up” statistical cluster analysis, within survey research on user behaviors and needs. Comparing the application of each method on the same data set shows that statistical cluster analysis can create rich data-driven personas that inform user needs and preferences and provide design teams with insightful recommendations in a short amount of time. This method also increases the potential for gaining unexpected information from quantitative data—an achievement typically viewed as within the purview of qualitative research alone. Using both approaches to the same dataset allowed us to both answer specific questions for the design team, and learn new insights from the bottom up.

scholarly journals Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements

2021 ◽  
Vol 10 (19) ◽  
pp. 4441
Author(s):  
Charat Thongprayoon ◽  
Carissa Y. Dumancas ◽  
Voravech Nissaisorakarn ◽  
Mira T. Keddis ◽  
Andrea G. Kattah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Kidney Disease ◽  
Serum Phosphate ◽  
Older Age ◽  
Consensus Clustering ◽  
Clustering Approach ◽  
Key Features ◽  
One Year ◽  
Cluster 2

Background: The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. Methods: We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster’s key features. We assessed the association of the clusters with mortality. Results: In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; a higher comorbidity burden, particularly hypertension; diabetes mellitus; coronary artery disease; lower eGFR; and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%; p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%; p < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; more primary admission for kidney disease; more history of hypertension; more end-stage kidney disease; more AKI at admission; and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 0.001), compared with Cluster 1. Conclusion: Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes.

Towards an improved understanding of nitrogen dioxide emissions from forest fires 

2021 ◽  
Author(s):  
Debora Griffin ◽  
Jack Chan ◽  
Enrico Dammers ◽  
Chris McLinden ◽  
Cristen Adams ◽  
...  
Keyword(s):  
Air Quality ◽  
Forest Fires ◽  
Synthetic Data ◽  
Estimation Methods ◽  
Wind Fields ◽  
Top Down ◽  
Vertical Column ◽  
Bottom Up ◽  
Fire Emissions ◽  
Direct Emission

&lt;p&gt;Smoke from wildfires are a significant source of air pollution, which can adversely impact ecosystems and the air quality in downwind populated areas. With increasing severity of wildfires over the years, these are a significant threat to air quality in densely populated areas. Emissions from wildfires are most commonly estimated by a bottom-up approach, using proxies such fuel type, burn area, and emission factors. Emissions are also commonly derived with a top-down approach, using satellite observed Fire Radiative Power. Furthermore, wildfire emissions can also be estimated directly from satellite-borne measurements.&lt;/p&gt;&lt;p&gt;Here, we present advancements and improvements of direct emission estimates of forest fire NO&lt;sub&gt;x&lt;/sub&gt; emissions by using TROPOMI (Tropospheric Monitoring Instrument) high-resolution satellite datasets, including NO&lt;sub&gt;2&lt;/sub&gt; vertical column densities (VCDs) and information on plume height and aerosol scattering. &amp;#160;The effect of smoke aerosols on the sensitivity of TROPOMI to NO&lt;sub&gt;2 &lt;/sub&gt;(via air mass factors) is estimated with recalculated VCDs, and validated with aircraft observations. Different top-down emission estimation methods are tested on synthetic data to determine the accuracy, and the sensitivity to parameters, such as wind fields, satellite sampling, instrument noise, NO&lt;sub&gt;2&lt;/sub&gt;:NO&lt;sub&gt;x&lt;/sub&gt; conversion ratio, species atmosphere lifetime and plume spread. Lastly, the top-down, bottom-up and direct emission estimates of fire emissions are quantitatively compared.&lt;/p&gt;

Psychology and Culture: Top Down Versus Bottom Up?

PsycCRITIQUES ◽  
2005 ◽  
Vol 50 (19) ◽  
Author(s):  
Michael Cole
Keyword(s):  
Top Down ◽  
Bottom Up

Optimalisasi Ekonomi Syariah Untuk Peningkatan Kesejahteraan Masyarakat Dalam Pengentasan Kemiskinan Mewujudkan Goodgovernance

2019 ◽  
Vol 4 (1) ◽  
pp. 19
Author(s):  
Sadari Sadari ◽  
Nurhidayat Nurhidayat ◽  
Rafiqah Rafiqah
Keyword(s):  
Top Down ◽  
Bottom Up

Humanisme religius telah mengantarkan pada era kesadaran bahwa peradaban manusia harus memiliki dua arus yang saling menunjang. Selama ini arus balik dalam bidang ekonomi hanya menonjolkan arus balik vertikal atas kebawah (model top down) yang didominasi oleh sistem ekonomi kapitalis dan sosialis, sedangkan di sisi lain mengesampingkan arus balik vertikal dari bawah ke atas (model bottom up) yang didominasi oleh sistem ekonomi syariah, sehingga dampaknya adalah adanya kesenjangan ekonomi yang sangat tajam. Paper ini mewujudkan peran penting, yakni menghubungkan dua arus tersebut secara timbal-balik, yakni mempertemukan arus pertama dengan arus balik kedua, sehingga akan menghasilkan dampak yang positif, progresif, kreatif dan produktif, kemudian pada akhirnya akan dapat meng-optomal-kan ekonomi syariah untuk menciptakan goodgovernance, post goodgovernance secara berkelanjutan, tentunya dengan bantuan peran media kontemporer yang kian update. Ekonomi syariah juga merupakan pilar dan nilai dasar, dari sikap keyakinan dan sikap rasionalitas untuk sanggup menciptakan terwujudnya pemberdayaan dan kesejahteraan sekaligus pengentasan kemiskinan dalam masyarakat di Indonesia.

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