Clustering Techniques

2022 ◽  
pp. 93-114
Author(s):  
Maxime C. Cohen ◽  
Paul-Emile Gras ◽  
Arthur Pentecoste ◽  
Renyu Zhang
Keyword(s):  
Clustering Techniques

Clustering techniques for thyroid nodules malignancy inference in the era of personalized medicine

2020 ◽  
Author(s):  
Andrea Giani ◽  
de Souza Patricia Borges ◽  
Stefania Bartoletti ◽  
Flavio Morselli ◽  
Andrea Conti ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Thyroid Nodules ◽  
Clustering Techniques

A State of Art Approaches on Energy Efficient Clustering Techniques in WSN

2019 ◽  
Vol 7 (3) ◽  
pp. 50-54
Author(s):  
N. Thilagavathi ◽  
Christy Wood ◽  
V. Hemalakshumi ◽  
V. Mathumiithaa
Keyword(s):  
Energy Efficient ◽  
Clustering Techniques ◽  
Energy Efficient Clustering ◽  
State Of Art

Systematic Defect Identification through Layout Snippet Clustering

2010 ◽  
Author(s):  
Wing Chiu Tam ◽  
Osei Poku ◽  
R. D. (Shawn) Blanton
Keyword(s):  
Design Process ◽  
Integrated Circuit ◽  
Yield Loss ◽  
Defect Identification ◽  
Clustering Techniques ◽  
Dominant Component

Abstract Systematic defects due to design-process interactions are a dominant component of integrated circuit (IC) yield loss in nano-scaled technologies. Test structures do not adequately represent the product in terms of feature diversity and feature volume, and therefore are unable to identify all the systematic defects that affect the product. This paper describes a method that uses diagnosis to identify layout features that do not yield as expected. Specifically, clustering techniques are applied to layout snippets of diagnosis-implicated regions from (ideally) a statistically-significant number of IC failures for identifying feature commonalties. Experiments involving an industrial chip demonstrate the identification of possible systematic yield loss due to lithographic hotspots.

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 Interactive Exploration of Large-Scale UI Datasets with Design Maps

2021 ◽  
Author(s):  
Luis A Leiva ◽  
Asutosh Hota ◽  
Antti Oulasvirta
Keyword(s):  
User Interface ◽  
Point Cloud ◽  
Large Scale ◽  
Mobile Apps ◽  
Online Resources ◽  
Design Tools ◽  
Support Design ◽  
Design Exploration ◽  
Clustering Techniques ◽  
Cloud Points

Abstract Designers are increasingly using online resources for inspiration. How to best support design exploration without compromising creativity? We introduce and study Design Maps, a class of point-cloud visualizations that makes large user interface datasets explorable. Design Maps are computed using dimensionality reduction and clustering techniques, which we analyze thoroughly in this paper. We present concepts for integrating Design Maps into design tools, including interactive visualization, local neighborhood exploration and functionality to integrate existing solutions to the design at hand. These concepts were implemented in a wireframing tool for mobile apps, which was evaluated with actual designers performing realistic tasks. Overall, designers find Design Maps supporting their creativity (avg. CSI score of 74/100) and indicate that the maps producing consistent whitespacing within cloud points are the most informative ones.

scholarly journals Clustering electricity market participants via FRM models

2020 ◽  
pp. 1-12
Author(s):  
Ayla Gülcü ◽  
Sedrettin Çalişkan
Keyword(s):  
Electricity Market ◽  
Distance Metrics ◽  
Agglomerative Clustering ◽  
Timely Manner ◽  
Short Term ◽  
Clustering Techniques ◽  
Market Participants ◽  
Cosine Distance ◽  
Collateral Mechanism

Collateral mechanism in the Electricity Market ensures the payments are executed on a timely manner; thus maintains the continuous cash flow. In order to value collaterals, Takasbank, the authorized central settlement bank, creates segments of the market participants by considering their short-term and long-term debt/credit information arising from all market activities. In this study, the data regarding participants’ daily and monthly debt payment and penalty behaviors is analyzed with the aim of discovering high-risk participants that fail to clear their debts on-time frequently. Different clustering techniques along with different distance metrics are considered to obtain the best clustering. Moreover, data preprocessing techniques along with Recency, Frequency, Monetary Value (RFM) scoring have been used to determine the best representation of the data. The results show that Agglomerative Clustering with cosine distance achieves the best separated clustering when the non-normalized dataset is used; this is also acknowledged by a domain expert.

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