Готові списки джерел за темами / Cluster clustering

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Добірка наукової літератури з теми "Cluster clustering"

Автор: Grafiati

Опубліковано: 1 квітня 2023

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Статті в журналах з теми "Cluster clustering"

Ahamad, Mohammed Gulam, Mohammed Faisal Ahmed, and Mohammed Yousuf Uddin. "Clustering as Data Mining Technique in Risk Factors Analysis of Diabetes, Hypertension and Obesity." European Journal of Engineering and Technology Research 1, no. 6 (July 27, 2018): 88–93. http://dx.doi.org/10.24018/ejeng.2016.1.6.202.

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This investigation explores data mining using open source software WEKA in health care application. The cluster analysis technique is utilized to study the effects of diabetes, obesity and hypertension from the database obtained from Virginia school of Medicine. The simple k-means cluster techniques are adopted to form ten clusters which are clearly discernible to distinguish the differences among the risk factors such as diabetes, obesity and hypertension. Cluster formation was tried by trial and error method and also kept the SSE as low as possible. The SSE is low when numbers of clusters are more. Less than ten clusters formation unable to yield distinguishable information. In this work each cluster is revealing quit important information about the diabetes, obesity, hypertension and their interrelation. Cluster 0: Diabetes ? Obesity ? Hypertension = Healthy patient, Cluster 1: Diabetes ? Obesity ? Hypertension = Healthy patient, Cluster2: Diabetes ? Obesity ? Hypertension = Obesity, Cluster3: Diabetes ? Obesity ? Hypertension = Patients with Obesity and Hypertension, Cluster4: Boarder line Diabetes ? Obesity ? Hypertension = Sever obesity, Cluster5: Obesity ? Hyper tension ? Diabetes = Hypertension, Cluster6: Border line obese ? Border line hypertension ? Diabetes = No serious complications, Cluster 7: Obesity ? Hypertension ? Diabetes= Healthy patients, Cluster 8: Obesity ? Hypertension ? Diabetes= Healthy patients, and Cluster 9: Diabetes ? Hyper tension ? Obesity = High risk unhealthy patients.

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Barnes, J., A. Dekel, G. Efstathiou, and C. S. Frenk. "Cluster-cluster clustering." Astrophysical Journal 295 (August 1985): 368. http://dx.doi.org/10.1086/163381.

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Rosing, K. E., and C. S. ReVelle. "Optimal Clustering." Environment and Planning A: Economy and Space 18, no. 11 (November 1986): 1463–76. http://dx.doi.org/10.1068/a181463.

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Cluster analysis can be performed with several models. One method is to seek those clusters for which the total flow between all within-cluster members is a maximum. This model has, until now, been viewed as mathematically difficult because of the presence of products of integer variables in the objective function. In another optimization model of cluster analysis, the p-median, a central member is found for each cluster, so that relationships of cluster members with the various central members are maximized (or minimized). This problem, although mathematically tractable, is a less realistic formulation of the general clustering problem. The formulation of the maximum interflow problem is here transformed in stages into a linear analogue which is economically solvable. Computation experience with the several transformed stages is reported and a practical example of the analysis demonstrated.

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Popkov, Yuri S., Yuri A. Dubnov, and Alexey Yu Popkov. "Entropy-Randomized Clustering." Mathematics 10, no. 19 (October 10, 2022): 3710. http://dx.doi.org/10.3390/math10193710.

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This paper proposes a clustering method based on a randomized representation of an ensemble of possible clusters with a probability distribution. The concept of a cluster indicator is introduced as the average distance between the objects included in the cluster. The indicators averaged over the entire ensemble are considered the latter’s characteristics. The optimal distribution of clusters is determined using the randomized machine learning approach: an entropy functional is maximized with respect to the probability distribution subject to constraints imposed on the averaged indicator of the cluster ensemble. The resulting entropy-optimal cluster corresponds to the maximum of the optimal probability distribution. This method is developed for binary clustering as a basic procedure. Its extension to t-ary clustering is considered. Some illustrative examples of entropy-randomized clustering are given.

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Baisya, Ritasman, Phani Kumar Devarasetti, Murthy G. S. R., and Liza Rajasekhar. "Autoantibody Clustering in Systemic Lupus Erythematosus–Associated Pulmonary Arterial Hypertension." Indian Journal of Cardiovascular Disease in Women - WINCARS 06, no. 02 (April 2021): 100–105. http://dx.doi.org/10.1055/s-0041-1732510.

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AbstractSystemic lupus erythematous–associated pulmonary arterial hypertension (SLE-PAH) is one of the important causes of mortality in lupus patients. Different autoantibodies are associated with SLE-PAH which can predict its future development. The objective of the study was to identify distinct autoantibody-based clusters in SLE-PAH patients and to compare demographic characters, clinical phenotypes, and therapeutic strategy across the clusters. Three distinct autoantibody clusters were identified using k-means cluster analysis in 71 SLE-PAH patients. Cluster1 had predominant Sm-RNP, Smith, SS-A association; cluster 2 had no definite autoantibody association; and cluster 3 was associated with nucleosome, histone, dsDNA, and ribosomal P protein. Patients in cluster 3 had a highly active disease while those in cluster 1 had significant cytopenia. Mean age and mean right ventricular systolic pressure (RVSP) were both high in cluster 2, indicating later-onset PAH in this group. This was the first autoantibody-based cluster analysis study in SLE-PAH patients in India which confirmed that autoantibodies did exist as clusters and the presence of definite autoantibodies can predict future development of pulmonary hypertension in these patients.

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Alfian, Muhammad, Ali Ridho Barakbah, and Idris Winarno. "Indonesian Online News Extraction and Clustering Using Evolving Clustering." JOIV : International Journal on Informatics Visualization 5, no. 3 (September 23, 2021): 280. http://dx.doi.org/10.30630/joiv.5.3.537.

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43,000 online media outlets in Indonesia publish at least one to two stories every hour. The amount of information exceeds human processing capacity, resulting in several impacts for humans, such as confusion and psychological pressure. This study proposes the Evolving Clustering method that continually adapts existing model knowledge in the real, ever-evolving environment without re-clustering the data. This study also proposes feature extraction with vector space-based stemming features to improve Indonesian language stemming. The application of the system consists of seven stages, (1) Data Acquisition, (2) Data Pipeline, (3) Keyword Feature Extraction, (4) Data Aggregation, (5) Predefined Cluster using Automatic Clustering algorithm, (6) Evolving Clustering, and (7) News Clustering Result. The experimental results show that Automatic Clustering generated 388 clusters as predefined clusters from 3.000 news. One of them is the unknown cluster. Evolving clustering runs for two days to cluster the news by streaming, resulting in a total of 611 clusters. Evolving clustering goes well, both updating models and adding models. The performance of the Evolving Clustering algorithm is quite good, as evidenced by the cluster accuracy value of 88%. However, some clusters are not right. It should be re-evaluated in the keyword feature extraction process to extract the appropriate features for grouping. In the future, this method can be developed further by adding other functions, updating and adding to the model, and evaluating.

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Cornell, John E., Jacqueline A. Pugh, John W. Williams, Jr, Lewis Kazis, Austin F. S. Lee, Michael L. Parchman, John Zeber, Thomas Pederson, Kelly A. Montgomery, and Polly Hitchcock Noël. "Multimorbidity Clusters: Clustering Binary Data From Multimorbidity Clusters: Clustering Binary Data From a Large Administrative Medical Database." Applied Multivariate Research 12, no. 3 (January 13, 2009): 163. http://dx.doi.org/10.22329/amr.v12i3.658.

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Our purpose in this article is to describe and illustrate the application of cluster analysis to identify clinically relevant multimorbidity groups. Multimorbidity is the co-occurrence of 2 or more illnesses within a single person, which raises the question whether consistent, clinically useful multimorbidity groups exist among sets of chronic illnesses. Our purpose in this article is to describe and illustrate the application of cluster analysis to identify clinically relevant multimorbidity groups. Application of cluster analysis involves a sequence of critical methodological and analytic decisions that influence the quality and meaning of the clusters produced. We illustrate the application of cluster analysis to identify multimorbidity clusters in a set of 45 chronic illnesses in primary care patients (N = 1,327,328), with 2 or more chronic conditions, served by the Veterans Health Administration. Six clinically useful multimorbidity clusters were identified: a Metabolic Cluster, an Obesity Cluster, a Liver Cluster, a Neurovascular Cluster, a Stress Cluster and a Dual Diagnosis Cluster. Cluster analysis appears to be a useful technique for identifying multiple disease clusters and patterns of multimorbidity.

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Li, Hong-Dong, Yunpei Xu, Xiaoshu Zhu, Quan Liu, Gilbert S. Omenn, and Jianxin Wang. "ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets." Journal of Bioinformatics and Computational Biology 18, no. 03 (June 2020): 2040009. http://dx.doi.org/10.1142/s0219720020400090.

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Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity between samples for clustering. While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets that most likely contribute to the clustering, thus facilitating biological interpretation. We compare ClusterMine with conventional approaches on nine real-world experimental datasets that represent different application scenarios in biology. We find that ClusterMine achieves better performances and that the gene sets prioritized by our method are biologically meaningful. ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php

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BORGELT, CHRISTIAN. "RESAMPLING FOR FUZZY CLUSTERING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 05 (October 2007): 595–614. http://dx.doi.org/10.1142/s0218488507004893.

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Resampling methods are among the best approaches to determine the number of clusters in prototype-based clustering. The core idea is that with the right choice for the number of clusters basically the same cluster structures should be obtained from subsamples of the given data set, while a wrong choice should produce considerably varying cluster structures. In this paper I give an overview how such resampling approaches can be transferred to fuzzy and probabilistic clustering. I study several cluster comparison measures, which can be parameterized with t-norms, and report experiments that provide some guidance which of them may be the best choice.

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Miyamoto, Sadaaki, Youhei Kuroda, and Kenta Arai. "Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (September 20, 2008): 448–53. http://dx.doi.org/10.20965/jaciii.2008.p0448.

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In addition to fuzzy c-means, possibilistic clustering is useful because it is robust against noise in data. The generated clusters are, however, strongly dependent on an initial value. We propose a family of algorithms for sequentially generating clusters “one cluster at a time,” which includes possibilistic medoid clustering. These algorithms automatically determine the number of clusters. Due to possibilistic clustering's similarity to the mountain clustering by Yager and Filev, we compare their formulation and performance in numerical examples.

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Дисертації з теми "Cluster clustering"

Dimitriadou, Evgenia, Andreas Weingessel, and Kurt Hornik. "A voting-merging clustering algorithm." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/94/1/document.pdf.

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In this paper we propose an unsupervised voting-merging scheme that is capable of clustering data sets, and also of finding the number of clusters existing in them. The voting part of the algorithm allows us to combine several runs of clustering algorithms resulting in a common partition. This helps us to overcome instabilities of the clustering algorithms and to improve the ability to find structures in a data set. Moreover, we develop a strategy to understand, analyze and interpret these results. In the second part of the scheme, a merging procedure starts on the clusters resulting by voting, in order to find the number of clusters in the data set.
Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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Al-Razgan, Muna Saleh. "Weighted clustering ensembles." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3212.

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Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 134. Thesis director: Carlotta Domeniconi. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed Oct. 14, 2008). Includes bibliographical references (p. 128-133). Also issued in print.

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Gaertler, Marco. "Clustering with spectral methods." [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10101213.

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Ptitsyn, Andrey. "New algorithms for EST clustering." Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&amp.

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Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology.

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Koepke, Hoyt Adam. "Bayesian cluster validation." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1496.

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We propose a novel framework based on Bayesian principles for validating clusterings and present efficient algorithms for use with centroid or exemplar based clustering solutions. Our framework treats the data as fixed and introduces perturbations into the clustering procedure. In our algorithms, we scale the distances between points by a random variable whose distribution is tuned against a baseline null dataset. The random variable is integrated out, yielding a soft assignment matrix that gives the behavior under perturbation of the points relative to each of the clusters. From this soft assignment matrix, we are able to visualize inter-cluster behavior, rank clusters, and give a scalar index of the the clustering stability. In a large test on synthetic data, our method matches or outperforms other leading methods at predicting the correct number of clusters. We also present a theoretical analysis of our approach, which suggests that it is useful for high dimensional data.

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Tittley, Eric Robert. "Hierarchical clustering and galaxy cluster scaling laws." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0008/NQ40291.pdf.

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Kuah, Adrian T. H. "Determinants of clustering, cluster growth and performance." Thesis, University of Manchester, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629921.

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Previous studies on industry clusters argue that there are benefits and externalities that only incumbents enjoy. Many studies focus on manufacturing and hightechnology clusters with less importance placed on services clusters. There are even fewer studies considering financial agglomerations as clusters that could create national competitive advantage. This thesis investigates the detenninants of clustering by focusing on the UK and Singapore financial sectors. It adopts an approach that concentrates on pure agglomerations - the importance of clientele, suppliers, factor conditions, rivalry, and how the agglomerations of competing and related industries influence incumbents' growth and perfonnance. The thesis combines two cluster models (Porter, 1990 and Swann et al., 1998) that contribute to an understanding of how, what and why certain detenninants lead to better perfonnance of finns in a cluster. It contributes to the existing literature by demonstrating that Porter's generic model is applicable to an international financial cluster and to another cluster in a smaller and open economy through the case studies of the London and Singapore financial centres. It advances knowledge on cluster theories by affinning Swann's lifetime growth model in the UK and Singapore financial clusters through econometric analysis, and extending the model to consider the causal and significant effects of cluster strengths in influencing finn perfonnance and the growth lifecycle of some 17,000 finns in the UK and Singapore. The thesis highlights the importance of factor and demand conditions in a cluster, and at the same time, the role that competitors and related industries play in enhancing the competitive advantage of the location. It illuminates the need for strong agglomerations of competing industry and related industries in a regional cluster to enhance incumbent's growth and actual financial perfonnance. Concertedly through the two cluster models, this thesis provides evidences of improved finn growth and actual financial perfonnance the more financial services finns cluster together, and how certain clustering conditions aid incumbents attain their competitive advantage.

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Shortreed, Susan. "Learning in spectral clustering /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8977.

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Chan, Alton Kam Fai. "Hyperplane based efficient clustering and searching /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202003%20CHANA.

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Madureira, Erikson Manuel Geraldo Vieira de. "Análise de mercado : clustering." Master's thesis, Instituto Superior de Economia e Gestão, 2016. http://hdl.handle.net/10400.5/13122.

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Mestrado em Decisão Económica e Empresarial
O presente trabalho tem como objetivo descrever as atividades realizadas durante o estágio efetuado na empresa Quidgest. Tendo a empresa a necessidade de estudar as suas diversas vertentes de negócio, optou-se por extrair e identificar as informações presentes no banco de dados da empresa. Para isso, foi utilizado um processo conhecido na análise de dados denominado por Extração de Conhecimento em Bases de Dados (ECBD). O maior desafio na utilização deste processo deveu-se há grande acumulação de informação pela empresa, que se foi intensificando a partir de 2013. Das fases do processo de ECBD, a que tem maior relevância é o data mining, onde é feito um estudo das variáveis caracterizadoras necessárias para a análise em foco. Foi escolhida a técnica de análise cluster da fase de data mining para que que toda análise possa ser eficiente, eficaz e se possa obter resultados de fácil leitura. Após o desenvolvimento do processo de ECBD, foi decidido que a fase de data mining podia ser implementada de modo a facilitar um trabalho futuro de uma análise realizada pela empresa. Para implementar essa fase, utilizaram-se técnicas de análise cluster e foi desenvolvida um programa em VBA/Excel centrada no utilizador. Para testar o programa criado foi utilizado um caso concreto da empresa. Esse caso consistiu em determinar quais os atuais clientes que mais contribuíram para a evolução da empresa nos anos de 2013 a 2015. Aplicando o caso referido no programa criado, obtiveram-se resultados e informações que foram analisadas e interpretadas.
This paper aims to describe the activities performed during the internship made in Quidgest company. Having the company need to study their various business areas, it was decided to extract and identify the information contained in the company's database. For this end, we used a process known in the data analysis called for Knowledge Discovery in Databases (KDD). The biggest challenge in using this process was due to their large accumulation of information by the company, which was intensified from 2013. The phases of the KDD process, which is the most relevant is data mining, where a study of characterizing variables required for the analysis is done. The cluster analysis technique of data mining phase was chosen for that any analysis can be efficient, effective and could provide results easy to read. After the development of the KDD process, it was decided that the data mining phase could be automated to facilitate future work carried out by the company. To automate this phase, cluster analysis techniques were used and was developed a program in VBA/Excel user-centered. To test the created program we used a specific case of the company. This case consisted in determining the current customers that have contributed to the company's evolution during the years 2013-2015. The application of the program has revealed useful information that has been analyzed and interpreted.
info:eu-repo/semantics/publishedVersion

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Книги з теми "Cluster clustering"

Xu, Rui. Clustering. Hoboken, N.J: Wiley, 2009.

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Phipps, Arabie, Hubert Lawrence J. 1944-, and Soete Geert de, eds. Clustering and classification. Singapore: World Scientific, 1996.

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Murtagh, Fionn. Multidimensional clustering algorithms. Vienna: Physica-Verlag, 1985.

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Miyamoto, Sadaaki. Algorithms for fuzzy clustering: Methods in c-means clustering with applications. Berlin: Springer, 2008.

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1968-, Abraham Ajith, and Konar Amit, eds. Metaheuristic clustering. Berlin: Springer, 2009.

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C, Dubes Richard, ed. Algorithms for clustering data. Englewood Cliffs, N.J: Prentice Hall, 1988.

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Mirkin, B. G. Mathematical classification and clustering. Dordrecht: Kluwer Academic Publishers, 1996.

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Janowitz, M. F. Ordinal and relational clustering. Singapore: World Scientific, 2010.

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Sergiy, Butenko, Chaovalitwongse W. Art, Pardalos P. M. 1954-, and DIMACS Workshop on Clustering Problems in Biological Networks (2006 : Rutgers University), eds. Clustering challenges in biological networks. New Jersry: World Scientific, 2009.

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Частини книг з теми "Cluster clustering"

Govaert, Gérard, and Mohamed Nadif. "Cluster Analysis." In Co-Clustering, 1–53. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118649480.ch1.

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Scitovski, Rudolf, Kristian Sabo, Francisco Martínez-Álvarez, and Šime Ungar. "Data Clustering." In Cluster Analysis and Applications, 31–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74552-3_3.

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Mirkin, Boris. "Single Cluster Clustering." In Nonconvex Optimization and Its Applications, 169–227. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0457-9_4.

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Price, P. B. "Cluster Radioactivity." In Clustering Phenomena in Atoms and Nuclei, 273–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02827-8_28.

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Bezdek, James C. "Probabilistic Clustering - GMD/EM." In Elementary Cluster Analysis, 193–223. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338086-9.

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Scitovski, Rudolf, Kristian Sabo, Francisco Martínez-Álvarez, and Šime Ungar. "Fuzzy Clustering Problem." In Cluster Analysis and Applications, 147–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74552-3_7.

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Scitovski, Rudolf, Kristian Sabo, Francisco Martínez-Álvarez, and Šime Ungar. "Mahalanobis Data Clustering." In Cluster Analysis and Applications, 117–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74552-3_6.

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Săndulescu, A., A. Ludu, and W. Greiner. "Cluster Radioactivity and Nuclear Structure-Clusters as Solitons." In Clustering Phenomena in Atoms and Nuclei, 262–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02827-8_27.

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Bezdek, James C. "Relational Clustering - The SAHN Models." In Elementary Cluster Analysis, 225–64. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338086-10.

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Bezdek, James C. "Clustering in Static Big Data." In Elementary Cluster Analysis, 325–78. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338086-13.

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Тези доповідей конференцій з теми "Cluster clustering"

Hauser, John R., and Syed A. Rizvi. "Cluster tool technology." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56620.

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Emeneker, Wesley, and Dan Stanzione. "Dynamic Virtual Clustering." In 2007 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2007. http://dx.doi.org/10.1109/clustr.2007.4629220.

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Zhang, Hongjing, and Ian Davidson. "Deep Descriptive Clustering." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/460.

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Recent work on explainable clustering allows describing clusters when the features are interpretable. However, much modern machine learning focuses on complex data such as images, text, and graphs where deep learning is used but the raw features of data are not interpretable. This paper explores a novel setting for performing clustering on complex data while simultaneously generating explanations using interpretable tags. We propose deep descriptive clustering that performs sub-symbolic representation learning on complex data while generating explanations based on symbolic data. We form good clusters by maximizing the mutual information between empirical distribution on the inputs and the induced clustering labels for clustering objectives. We generate explanations by solving an integer linear programming that generates concise and orthogonal descriptions for each cluster. Finally, we allow the explanation to inform better clustering by proposing a novel pairwise loss with self-generated constraints to maximize the clustering and explanation module's consistency. Experimental results on public data demonstrate that our model outperforms competitive baselines in clustering performance while offering high-quality cluster-level explanations.

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Venkataraman, P. "Data Clustering Using the Natural Bézier Functions." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85103.

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An unorthodox and effective non-iterative procedure for spherical clusters is demonstrated in this paper. It uses natural Bézier functions to determine initial cluster locations using the content of the data. The natural Bernstein-Bézier functions are very robust in representing data through continuous functions in the application of functional data analysis. This paper demonstrates that they are equally robust at resolving data clusters in classification problems. The original data is scaled and segmented. A natural Bézier function is fitted for each segment and the initial clusters are centered at the function extremums that are distinctly located. A self-selection process based on least distance is used to assign the data to these initial cluster centers. A minimum membership count is imposed and nearby clusters are combined to reduce these initial cluster centers based on visual clues. Centroid recalculation and data reassignment can be used for centroid convergence. The method is demonstrated for two dimensional spherical clusters. This approach requires no iteration. Four examples from clustering benchmark datasets are used to showcase the method. The data include different numbers of clusters, different data density for the clusters, as well as different levels of overlap. This method is new and different from other data clustering methods available in the literature. It is better than the standard k-means clustering method since it does not require information on the number of clusters or cluster membership count. The method is non-iterative and does not require random initialization or distance optimization.

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Arıcıoğlu, Mustafa Atilla, and Yasemin Savaş. "Clustering Policies in Japan as an Example of Clustering Strategy." In International Conference on Eurasian Economies. Eurasian Economists Association, 2021. http://dx.doi.org/10.36880/c13.02567.

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Clustering as a competitive tool allows companies to be in an advantageous position in the sector by cooperating on various issues, especially the exchange of information with each other. Organizations move forward with the cooperation they develop through clusters. In the literature, it has been seen that clusters are considered as a strategy and Competition model tool, considering the benefits they provide. In this study, the concept of clustering is explained within the framework of the concepts of trust and cooperation. Cluster expectations and cooperation in cluster networks are maintained according to the trust relationship between them. In the studies on this subject, it is observed that the clustering policies in Japan, which successfully implement cooperation as a strategy in accordance with the obligations of mutual trust, are taken as an example. For this reason, research on the clustering policies of Japan was included in the continuation of the study. It is believed that the study will contribute to the literature with conceptual explanations.

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Seidel, Thomas E., and Michael R. Stark. "Learning opportunities through the use of cluster tools." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56617.

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Li, Jia, Dongsheng Li, and Yiming Zhang. "Efficient Distributed Data Clustering on Spark." In 2015 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2015. http://dx.doi.org/10.1109/cluster.2015.84.

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Dey, Sayak, Swagatam Das, and Rammohan Mallipeddi. "The Sparse MinMax k-Means Algorithm for High-Dimensional Clustering." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/291.

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Classical clustering methods usually face tough challenges when we have a larger set of features compared to the number of items to be partitioned. We propose a Sparse MinMax k-Means Clustering approach by reformulating the objective of the MinMax k-Means algorithm (a variation of classical k-Means that minimizes the maximum intra-cluster variance instead of the sum of intra-cluster variances), into a new weighted between-cluster sum of squares (BCSS) form. We impose sparse regularization on these weights to make it suitable for high-dimensional clustering. We seek to use the advantages of the MinMax k-Means algorithm in the high-dimensional space to generate good quality clusters. The efficacy of the proposal is showcased through comparison against a few representative clustering methods over several real world datasets.

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Zhao, Han, Xu Yang, Zhenru Wang, Erkun Yang, and Cheng Deng. "Graph Debiased Contrastive Learning with Joint Representation Clustering." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/473.

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By contrasting positive-negative counterparts, graph contrastive learning has become a prominent technique for unsupervised graph representation learning. However, existing methods fail to consider the class information and will introduce false-negative samples in the random negative sampling, causing poor performance. To this end, we propose a graph debiased contrastive learning framework, which can jointly perform representation learning and clustering. Specifically, representations can be optimized by aligning with clustered class information, and simultaneously, the optimized representations can promote clustering, leading to more powerful representations and clustering results. More importantly, we randomly select negative samples from the clusters which are different from the positive sample's cluster. In this way, as the supervisory signals, the clustering results can be utilized to effectively decrease the false-negative samples. Extensive experiments on five datasets demonstrate that our method achieves new state-of-the-art results on graph clustering and classification tasks.

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Seidel, J. P., W. Wachter, William M. Triggs, and Robert P. Hall. "Integrated deposition of TiN barrier layers in cluster tools." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56619.

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Звіти організацій з теми "Cluster clustering"

Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For the correct selection of clusters, a comparative analysis of several methods was performed: arithmetic mean ranks, hierarchical methods followed by dendrogram construction, K- means method, which refers to reference methods, in which the number of groups is specified by the user. The cluster analysis of forestries by twenty analytical grounds was not proved by analysis of variance, so the re-clustering of certain objects was carried out according to the nine most significant analytical features. As a result, the forestry was clustered into four clusters. The conducted cluster analysis with the use of different methods allows us to state that their combination helps to select reasonable groupings, clearly illustrate the clustering procedure and rank the obtained forestry clusters.

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Ravindran, Vijay, and Chockalingam Vannila. An Energy-efficient Clustering Protocol for IoT Wireless Sensor Networks Based on Cluster Supervisor Management. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, December 2021. http://dx.doi.org/10.7546/crabs.2021.12.12.

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Wang, Li, Vu Li, Huang Deng, and Chu Pan. Existence non-commutative clustering methods for optimizing a load of processor cores for multiple marking of percolation cluster algorithm. Web of Open Science, February 2020. http://dx.doi.org/10.37686/ser.v1i1.3.

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Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.79.

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In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means

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Rutherford, J., and J. F. Cassidy. Comparing felt intensity patterns for crustal earthquakes in the Cascadia and Chilean subduction zones, offshore British Columbia, United States, and Chile. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330475.

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In this study, we utilize US Geological Survey citizen science earthquake felt intensity data to investigate whether , crustal earthquakes in the Chilean Subduction Zone show similar, "felt intensity" distributions to events of the same magnitude and depths within the Cascadia Subduction Zone (Quitoriano & Wald, 2020; USGS Earthquake Hazards Program, 2020). In a companion article (Rutherford & Cassidy, 2022) we examine intraslab deep earthquake intensity patterns for the Chile and Cascadia subduction zones. Building on from the intraslab companion article, the goal of this comparison is to determine whether felt intensity information from several recent large (M8-8.8) subduction earthquakes in Chile can be applied to Cascadia (where no subduction earthquakes have been felt since 1700). This would provide a better understanding of shaking intensity patterns for future subduction earthquakes in Cascadia - critical information for scientists, engineers, and emergency management organizations. For this research, we utilized 20 years of cataloged Did-You-Feel-It (DYFI) citizen science data from the US Geological Survey's (USGS) earthquake online catalog, the ANSS Comprehensive Earthquake Catalog (ComCat) Documentation (USGS Earthquake Hazards Program, 2021). In total, we considered and compared intensity patterns for fourteen magnitudes from 30 earthquakes in Cascadia (ranging from magnitudes 4.5 to 7.2, the highest magnitude event in Cascadia zone) to the intensity patterns from 114 earthquakes in Chile, with the same magnitudes as the Cascadia events (M4.5-M7.2). Our analysis involved plotting and fitting the Chile and Cascadia earthquake DYFI responses to compare the intensity patterns for the two subduction zones. Overall, we find good agreement between felt patterns in Chile and Cascadia. For example, all plots show the expected downward trend for intensity with distance. Even distribution with limited clustering is seen in all fourteen magnitudes, with slight intensity clustering of responses around the 30 to 600 km. This is slightly different from the intraslab pattern which demonstrated a distinct cluster at further distance from the hypocenter, e.g., cluster at 50 to 300 km. These results provide confidence that we can use Chilean intensity data for megathrust earthquakes in Cascadia.

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Fraley, Chris, Adrian Raftery, and Ron Wehrensy. Incremental Model-Based Clustering for Large Datasets With Small Clusters. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada459790.

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Russo, Margherita, Fabrizio Alboni, Jorge Carreto Sanginés, Manlio De Domenico, Giuseppe Mangioni, Simone Righi, and Annamaria Simonazzi. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts. Institute for New Economic Thinking Working Paper Series, January 2022. http://dx.doi.org/10.36687/inetwp173.

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In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional con-tent in the production of automotive components and parts traded between the three part-ner countries, United States, Canada and Mexico. Signed by all three countries, the new trade agreement, USMCA, is to go into force in 2022. Nonetheless, after the 2020 Presi-dential election, the new treaty's future is under discussion, and its impact on the automo-tive industry is not entirely defined. Another significant shift in this industry – the acceler-ated rise of electric vehicles – also occurred in 2020: while the COVID-19 pandemic largely halted most plants in the automotive value chain all over the world, at the reopen-ing, the tide is now running against internal combustion engine vehicles, at least in the an-nouncements and in some large investments planned in Europe, Asia and the US. The definition of the pre-pandemic situation is a very helpful starting point for the analysis of the possible repercussions of the technological and geo-political transition, which has been accelerated by the epidemic, on geographical clusters and sectorial special-isations of the main regions and countries. This paper analyses the trade networks emerg-ing in the past 25 years in a new analytical framework. In the economic literature on inter-national trade, the study of the automotive global value chains has been addressed by us-ing network analysis, focusing on the centrality of geographical regions and countries while largely overlooking the contribution of countries' bilateral trading in components and parts as structuring forces of the subnetwork of countries and their specific position in the overall trade network. The paper focuses on such subnetworks as meso-level structures emerging in trade network over the last 25 years. Using the Infomap multilayer clustering algorithm, we are able to identify clusters of countries and their specific trades in the automotive internation-al trade network and to highlight the relative importance of each cluster, the interconnec-tions between them, and the contribution of countries and of components and parts in the clusters. We draw the data from the UN Comtrade database of directed export and import flows of 30 automotive components and parts among 42 countries (accounting for 98% of world trade flows of those items). The paper highlights the changes that occurred over 25 years in the geography of the trade relations, with particular with regard to denser and more hierarchical network gener-ated by Germany’s trade relations within EU countries and by the US preferential trade agreements with Canada and Mexico, and the upsurge of China. With a similar overall va-riety of traded components and parts within the main clusters (dominated respectively by Germany, US and Japan-China), the Infomap multilayer analysis singles out which com-ponents and parts determined the relative positions of countries in the various clusters and the changes over time in the relative positions of countries and their specialisations in mul-tilateral trades. Connections between clusters increase over time, while the relative im-portance of the main clusters and of some individual countries change significantly. The focus on US and Mexico and on Germany and Central Eastern European countries (Czech Republic, Hungary, Poland, Slovakia) will drive the comparative analysis.

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DAVID A. BOOTHMAN, Ph D. Clusterin: an IR-inducible protein determining life and death. Office of Scientific and Technical Information (OSTI), July 2006. http://dx.doi.org/10.2172/886107.

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Leskov, Konstantin S., and David A. Boothman. The Role of Clusterin in Estrogen Deprivation-Mediated Cell Death in Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada407480.

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Criswell, Tracy L., and David A. Boothman. Investigating the Role of Nuclear Clusterin (nCLU) in Lethality and Genomic Instability in Paclitaxel (Taxol) - Treated Human Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada406785.

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