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MONTE-CARLO COMPARISON OF 6 hierarchical-clustering methods ON RANDOM DATA

PATTERN RECOGNITION 1986年第1期19卷 95-99页

作者： JAIN, NC INDRAYAN, A GOEL, LR UNIV COLL MED SCI NEW DELHIINDIA MINIST PLANNING CTR COMPDEPT STATNEW DELHIINDIA MEERUT UNIV MEERUT 250001UTTAR PRADESHINDIA

There is mounting evidence to suggest that the complete linkage method does the best clustering job among all hierarchical agglomerative techniques, particularly with respect to misclassification in samples from known multivariate normal distributions. However, clustering methods are notorious for discovering clusters on random data sets also. We compare six agglomerative hierarchical methods on univariate random data from uniform and standard normal distributions and find that the complete linkage method generally is best in not discovering false clusters. The criterion is the ratio of number of within-cluster distances to number of all distances at most equal to the maximum within-cluster distance.

关键词： hierarchical clustering methods Univariate random data Within-cluster distance Between-cluster distance Monte Carlo simulation Complete linkage method

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Isotopic and Statistical methods for the Traceability of Milk and Dairy Products

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FOOD ANALYTICAL methods 2022年第7期15卷 1936-1944页

作者： Schipilliti, Luisa Bonaccorsi, Ivana Consolo, Giancarlo Mondello, Luigi Univ Messina Dipartimento Sci Chim Biol Farmaceut & Ambientali Messina Italy Univ Messina Dipartimento Sci Matemat & Informat Sci Fis & Sci Messina Italy

Milk composition is dependent on the mammalian's specie and on its diet. In particular, the fatty acid distribution is strongly dependent on the species and is significantly correlated to the type of forage used to feed animals. In this work, the Comprehensive Isotopic Data Evaluation (CIDE) method, applied to the carbon isotope ratio of different fatty acids (delta C-13(FAs) values) of milk and dairy products, is shown to be suitable to identify the prevalent type of forage used to feed the animals and, at the same type, the animal breed. Results of isotopic analysis are validated by agglomerative hierarchical clustering methods, providing the reliability of the proposed experimental approach. The strategy here discussed might be also useful to defend Protected Designation of Origin (PDO) dairy products, such as "Mozzarella di Bufala Campana."

关键词： Gas chromatography isotope mass spectrometry hierarchical clustering methods Milk and dairy products C3/C4 plants

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Comparison of hierarchical cluster analysis methods by cophenetic correlation

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JOURNAL OF INEQUALITIES AND APPLICATIONS 2013年第1期2013卷 1-8页

作者： Saracli, Sinan Dogan, Nurhan Dogan, Ismet Afyon Kocatepe Univ Fac Arts & Sci Dept Stat TR-03200 Afyon Turkey Afyon Kocatepe Univ Fac Med Dept Biostat TR-03200 Afyon Turkey

This study proposes the best clustering method(s) for different distance measures under two different conditions using the cophenetic correlation coefficient. In the first one, the data has multivariate standard normal distribution without outliers for and the second one is with outliers (5%) for . The proposed method is applied to simulated multivariate normal data via MATLAB software. According the results of simulation the Average (especially for ) and Centroid (especially for and ) methods are recommended at both conditions. This study hopes to contribute to literature for making better decisions on selection of appropriate cluster methods by using subgroup sizes, variable numbers, subgroup means and variances.

关键词： cophenetic correlation hierarchical clustering methods distance measures

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Data Mining Approaches for the methods to Minimize Total Tardiness in Parallel Machine Scheduling Problem

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IFAC-PapersOnLine 2016年第12期49卷 431-436页

作者： Senvar, Ozlem Yalaoui, Farouk Dugardin, Frédéric Bernate Lara, Andres Felipe UMR CNRS 6281 Université de Technologie de Troyes grantee Ref.2013/327-350 12 rue Marie Curie CS42060 Troyes cedex10004 France

This study examines large sample size of instances and tries to extract useful knowledge about the domain of parallel machine scheduling problem (PMSP) and solution space explored. The aim of this study is to provide statistical interpretations and classify the differences between the instances. The interrelationship between specified predetermined inputs and the output is examined through artificial neural networks (ANNs) along with regression analysis since they can easily explore which inputs are related to the output and develop regression model. The results of both analyses reveal significancies of the relationships and predicted importance of the predetermined inputs on the output. Furthermore, we examined the behaviours or patterns of the instances, after realizing the easiness and hardiness of the instances accentuating the differences. In order to link the predetermined inputs of instances with the performances of the set of tested methods, the differences between instances are evaluated in terms of variability. Then, we grouped instances into three clusters, specifying as exact, equal and difficult zones, for information retrieval about their complexities via hierarchical clustering method. © 2016

关键词： Data mining Cluster analysis Machinery Neural networks Regression analysis Scheduling Agglomerative hierarchical clustering hierarchical clustering methods Parallel machine scheduling problems Regression model Sample sizes Solution space Statistical interpretation Total tardiness

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clustering methods for the efficient voltage regulation in smart grids

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JOURNAL OF ENGINEERING-JOE 2022年第3期2022卷 274-284页

作者： Abegaz, Brook W. Loyola Univ Engn Dept Chicago IL 60611 USA

In this paper, clustering methods are presented to enhance the stability of automatic voltage regulators using the efficient adjustment of their respective gains. The results show that implementations of some of the clustering algorithms provide better reliability and stability for the feedback-based voltage regulators as compared to the other methods, namely, a model predictive controller (MPC), a gaussian mixture model (GMM), a self-organizing mapping (SOM) and hierarchical clustering (HC) methods. Specifically, the K-Means clustering approach (KM) provided superior stability but a slower rise time of the output voltage of the voltage regulators as compared to the other methods. Furthermore, coordination of the clustering methods is tested for a 10 machine, 39 bus power grid system. The results show that the clustering approach could be applied to improve the efficiency of voltage regulation methods in smart grids and related cyber-physical systems.

关键词： voltage regulators pattern clustering predictive control bus power grid system SOM cyber-physical systems hierarchical clustering methods MPC feedback-based voltage regulator stability power system stability Gaussian processes cyber-physical system HC methods smart power grids power system control Gaussian mixture model smart grids voltage control power engineering computing control engineering computing voltage regulation self-organizing mapping self-organising feature maps model predictive controller feedback-based voltage regulator reliability power system reliability

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An Approach to Grouping Traffic Signals for Coordination Using clustering methods 5

An Approach to Grouping Traffic Signals for Coordination Usi...

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5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Lee, Minha Chang, Hyoseuk Carrion, Carlos Zhang, Lei Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA

ISBN: (纸本)9781509064847

Signal coordination is one of the most widely employed strategies to efficiently operate signal plans in a network. The impact on a network varies depending on signal plans as well as the spatial shape of coordination groups. Improper coordination groups may worsen the traffic flow and level of service Finding ideal decomposition points of an arterial is critical in a system-wide perspective. This study aims to propose a grouping method that identifies ideal breaking points for signalized corridors to improve the performance while minimizing traffic operators' subjective decision.

关键词： signal coordination intersection grouping hierarchical clustering methods signal decomposition method

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Cluster structure inference based on clustering stability with applications to microarray data analysis

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EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING 2004年第1期2004卷 64-80页

作者： Giurcaneanu, CD Tabus, I Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland

This paper focuses on the stability-based approach for estimating the number of clusters K in microarray data. The cluster stability approach amounts to performing clustering successively over random subsets of the available data and evaluating an index which expresses the similarity of the successive partitions obtained. We present a method for automatically estimating K by starting from the distribution of the similarity index. We investigate how the selection of the hierarchical clustering (HQ method, respectively, the similarity index, influences the estimation accuracy. The paper introduces a new similarity index based on a partition distance. The performance of the new index and that of other well-known indices are experimentally evaluated by comparing the "true" data partition with the partition obtained at each level of an HC tree. A case study is conducted with a publicly available Leukemia dataset.

关键词： clustering stability number of clusters hierarchical clustering methods similarity indices partition-distance microarray data

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A monothetic clustering method

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PATTERN RECOGNITION LETTERS 1998年第11期19卷 989-996页

作者： Chavent, M Inst Natl Rech Informat & Automat Act SODAS F-78153 Le Chesnay France Univ Paris 09 Lise Ceremade F-75775 Paris 16 France

The proposed divisive clustering method performs simultaneously a hierarchy of a set of objects and a monothetic characterization of each cluster of the hierarchy. A division is performed according to the within-cluster inertia criterion which is minimized among the bipartitions induced by a set of binary questions. In order to improve the clustering, the algorithm revises at each step the division which has induced the cluster chosen for division. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： hierarchical clustering methods monothetic cluster inertia criterion

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On the stability of hierarchical classification: Qualitative approaches

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MATHEMATICAL AND COMPUTER MODELLING 2009年第3-4期50卷 329-332页

作者： Barthelemy, Jean-Pierre Gusho, Gentian Ecole Natl Super Telecommun Bretagne Dept Log Usages Sci Sociales & Informat CS 83818 F-29238 Brest France ENST Bretagne CNRS UMR 2872 TAMCIC Paris France EHESS CNRS UMR 8557 Ctr Analyse & Math Sociale F-75270 Paris 06 France

The aim of this paper is to study various forms of stability of clustering methods in a hierarchical and qualitative framework. It is based on two notions of the restriction of a hierarchy to a subset of objects. The first section discusses some properties of these notions of restriction. The second section gives ten axioms to describe the notion of stability. The last section establishes an impossibility theorem: there is no stable clustering method (except for very small cases). (C) 2009 Elsevier Ltd. All rights reserved.

关键词： hierarchical clustering hierarchical clustering methods Stability by restriction and removal Impossibility theorems

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Identifying representative days of solar irradiance and wind speed in Brazil using machine learning techniques

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Energy and AI 2024年第1期15卷 151-170页

作者： Rafaela Ribeiro Bruno Fanzeres Industrial Engineering Department Pontifical Catholic University of Rio de Janeiro(PUC-Rio)22451-900Rio de JaneiroRJBrazil

The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades,following the worldwide-seeking-goal of a carbon-neutral economy and the country’s incentives in diversifying its generation *** a long-term perspective,the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the *** this context,the main objective of this work is to provide a thorough descriptive analytics of the time-linked hourly-based daily dynamics of wind speed and solar irradiance in the main resourceful regions of *** on unsupervised Machine Learning methods,we focus on identifying similar days over the years,Representative Days,that can depict the fundamental underlying behaviour of each *** analysis is based on a historical dataset of different sites with the highest potential and installed capacity of each source spread over the country:three in the Northeast and one in the South Regions,for wind speed;and three in the Northeast and one in the Southeast Regions,for solar *** use two Partitioning methods(𝐾-Means and𝐾-Medoids),the hierarchical Ward’s Method,and a Model-Based Method(Self-Organizing Maps).We identified that wind speed and solar irradiance can be effectively represented,respectively,by only two representative days,and two or three days,depending on the region and method(segments data with respect to the intensity of each source).Analysis with higher Representative Days highlighted important hidden patterns such as different wind speed modulations and solar irradiance peak-hours along the days.

关键词： Partitioning clustering methods hierarchical clustering methods Model-based clustering methods Representative days Solar irradiance Wind speed

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