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Relevant bibliographies by topics / Clustering validation measures

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Academic literature on the topic 'Clustering validation measures'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Clustering validation measures"

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Brun, Marcel, Chao Sima, Jianping Hua, et al. "Model-based evaluation of clustering validation measures." Pattern Recognition 40, no. 3 (2007): 807–24. http://dx.doi.org/10.1016/j.patcog.2006.06.026.

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Campagner, Andrea, and Davide Ciucci. "Orthopartitions and soft clustering: Soft mutual information measures for clustering validation." Knowledge-Based Systems 180 (September 2019): 51–61. http://dx.doi.org/10.1016/j.knosys.2019.05.018.

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Manjunath, Mohith, Yi Zhang, Yeonsung Kim, et al. "ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data." PeerJ Computer Science 4 (May 21, 2018): e155. http://dx.doi.org/10.7717/peerj-cs.155.

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Background Clustering is one of the most common techniques in data analysis and seeks to group together data points that are similar in some measure. Although there are many computer programs available for performing clustering, a single web resource that provides several state-of-the-art clustering methods, interactive visualizations and evaluation of clustering results is lacking. Methods ClusterEnG (acronym for Clustering Engine for Genomics) provides a web interface for clustering data and interactive visualizations including 3D views, data selection and zoom features. Eighteen clustering

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Yanchi Liu, Zhongmou Li, Hui Xiong, Xuedong Gao, Junjie Wu, and Sen Wu. "Understanding and Enhancement of Internal Clustering Validation Measures." IEEE Transactions on Cybernetics 43, no. 3 (2013): 982–94. http://dx.doi.org/10.1109/tsmcb.2012.2220543.

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Panskyi, Taras, and Volodymyr Mosorov. "A STEP TOWARDS THE MAJORITY-BASED CLUSTERING VALIDATION DECISION FUSION METHOD." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 11, no. 2 (2021): 4–13. http://dx.doi.org/10.35784/iapgos.2596.

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A variety of clustering validation indices (CVIs) aimed at validating the results of clustering analysis and determining which clustering algorithm performs best. Different validation indices may be appropriate for different clustering algorithms or partition dissimilarity measures; however, the best suitable index to use in practice remains unknown. A single CVI is generally unable to handle the wide variability and scalability of the data and cope successfully with all the contexts. Therefore, one of the popular approaches is to use a combination of multiple CVIs and fuse their votes into th

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Hui Xiong, Junjie Wu, and Jian Chen. "K-Means Clustering Versus Validation Measures: A Data-Distribution Perspective." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, no. 2 (2009): 318–31. http://dx.doi.org/10.1109/tsmcb.2008.2004559.

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Ping Luo, Hui Xiong, Guoxing Zhan, Junjie Wu, and Zhongzhi Shi. "Information-Theoretic Distance Measures for Clustering Validation: Generalization and Normalization." IEEE Transactions on Knowledge and Data Engineering 21, no. 9 (2009): 1249–62. http://dx.doi.org/10.1109/tkde.2008.200.

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Gao, Xuedong, and Minghan Yang. "Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data." Algorithms 11, no. 11 (2018): 177. http://dx.doi.org/10.3390/a11110177.

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Clustering is one of the main tasks of machine learning. Internal clustering validation indexes (CVIs) are used to measure the quality of several clustered partitions to determine the local optimal clustering results in an unsupervised manner, and can act as the objective function of clustering algorithms. In this paper, we first studied several well-known internal CVIs for categorical data clustering, and proved the ineffectiveness of evaluating the partitions of different numbers of clusters without any inter-cluster separation measures or assumptions; the accurateness of separation, along w

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Son, Le Hoang, and Pham Van Hai. "A Novel Multiple Fuzzy Clustering Method Based on Internal Clustering Validation Measures with Gradient Descent." International Journal of Fuzzy Systems 18, no. 5 (2015): 894–903. http://dx.doi.org/10.1007/s40815-015-0117-1.

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Wu, Junjie, Jian Chen, Hui Xiong, and Ming Xie. "External validation measures for K-means clustering: A data distribution perspective." Expert Systems with Applications 36, no. 3 (2009): 6050–61. http://dx.doi.org/10.1016/j.eswa.2008.06.093.

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Dissertations / Theses on the topic "Clustering validation measures"

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Jaskowiak, Pablo Andretta. "On the evaluation of clustering results: measures, ensembles, and gene expression data analysis." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-23032016-111454/.

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Clustering plays an important role in the exploratory analysis of data. Its goal is to organize objects into a finite set of categories, i.e., clusters, in the hope that meaningful and previously unknown relationships will emerge from the process. Not every clustering result is meaningful, though. In fact, virtually all clustering algorithms will yield a result, even if the data under analysis has no true clusters. If clusters do exist, one still has to determine the best configuration of parameters for the clustering algorithm in hand, in order to avoid poor outcomes. This selection is usuall

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Mello, Paula Lunardi de. "Sistemáticas de agrupamento de países com base em indicadores de desempenho." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/158359.

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A economia mundial passou por grandes transformações no último século, as quais incluiram períodos de crescimento sustentado seguidos por outros de estagnação, governos alternando estratégias de liberalização de mercado com políticas de protecionismo comercial e instabilidade nos mercados, dentre outros. Figurando como auxiliar na compreensão de problemas econômicos e sociais de forma sistêmica, a análise de indicadores de desempenho é capaz de gerar informações relevantes a respeito de padrões de comportamento e tendências, além de orientar políticas e estratégias para incremento de resultado

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Kota, Sai Mohan Harsha. "Analysis of Organizational Structure of a Company by Evaluation of Email Communications of Employees : A Case Study." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15933.

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There are many aspects that govern the performance of an organization. One of the most important thing is their organizational structure. Having a well-planned organizational structure facilitates good internal communication among the employees, which in turn contributes to the success of the organization. Today, company re-structuring is very common in the industry. When various key employees are re-organized (moved to different hierarchical positions), the company might experience certain incidents which can be damaging or beneficial for the company. To leverage the potential gain, having an

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Riedl, Pavel. "Modul shlukové analýzy systému pro dolování z dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237095.

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This master's thesis deals with development of a module for a data mining system, which is being developed on FIT. The first part describes the general knowledge discovery process and cluster analysis including cluster validation; it also describes Oracle Data Mining including algorithms, which it uses for clustering. At the end it deals with the system and the technologies it uses, such as NetBeans Platform and DMSL. The second part describes design of a clustering module and a module used to compare its results. It also deals with visualization of cluster analysis results and shows the achie

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TEMBE, WAIBHAV DEEPAK. "PATTERN EXTRACTION USING A CONTEXT DEPENDENT MEASURE OF DIVERGENCE AND ITS VALIDATION." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin994882030.

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Fu, Xuezheng. "Structure Pattern Analysis Using Term Rewriting and Clustering Algorithm." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/17.

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Biological data is accumulated at a fast pace. However, raw data are generally difficult to understand and not useful unless we unlock the information hidden in the data. Knowledge/information can be extracted as the patterns or features buried within the data. Thus data mining, aims at uncovering underlying rules, relationships, and patterns in data, has emerged as one of the most exciting fields in computational science. In this dissertation, we develop efficient approaches to the structure pattern analysis of RNA and protein three dimensional structures. The major techniques used in this wo

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Book chapters on the topic "Clustering validation measures"

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Wu, Junjie. "Selecting External Validation Measures for K-means Clustering." In Advances in K-means Clustering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29807-3_5.

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Draszawka, Karol, and Julian Szymański. "External Validation Measures for Nested Clustering of Text Documents." In Emerging Intelligent Technologies in Industry. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22732-5_18.

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Nerurkar, Pranav, Aruna Pavate, Mansi Shah, and Samuel Jacob. "Performance of Internal Cluster Validations Measures For Evolutionary Clustering." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1513-8_32.

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Nerurkar, Pranav, Aruna Pavate, Mansi Shah, and Samuel Jacob. "Correction to: Performance of Internal Cluster Validations Measures For Evolutionary Clustering." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1513-8_105.

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Xiong, Hui, and Zhongmou Li. "Clustering Validation Measures." In Data Clustering. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315373515-23.

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Honda, K., A. Notsu, T. Matsui, and H. Ichihashi. "Fuzzy Cluster Validation Based on Fuzzy PCA-Guided Procedure." In Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1870-1.ch002.

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Cluster validation is an important issue in fuzzy clustering research and many validity measures, most of which are motivated by intuitive justification considering geometrical features, have been developed. This paper proposes a new validation approach, which evaluates the validity degree of cluster partitions from the view point of the optimality of objective functions in FCM-type clustering. This approach makes it possible to evaluate the validity degree of robust cluster partitions, in which geometrical features are not available because of their possibilistic natures.

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Kumar, Pradeep Kumar, Raju S. Bapi, and P. Radha Krishna. "SeqPAM." In Successes and New Directions in Data Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-645-7.ch002.

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With the growth in the number of web users and necessity for making information available on the web, the problem of web personalization has become very critical and popular. Developers are trying to customize a web site to the needs of specific users with the help of knowledge acquired from user navigational behavior. Since user page visits are intrinsically sequential in nature, efficient clustering algorithms for sequential data are needed. In this paper, we introduce a similarity preserving function called sequence and set similarity measure S3M that captures both the order of occurrence of page visits as well as the content of pages. We conducted pilot experiments comparing the results of PAM, a standard clustering algorithm, with two similarity measures: Cosine and S3M. The goodness of the clusters resulting from both the measures was computed using a cluster validation technique based on average levensthein distance. Results on pilot dataset established the effectiveness of S3M for sequential data. Based on these results, we proposed a new clustering algorithm, SeqPAM for clustering sequential data. We tested the new algorithm on two datasets namely, cti and msnbc datasets. We provided recommendations for web personalization based on the clusters obtained from SeqPAM for msnbc dataset.

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Kumar, Pradeep, Raju S. Bapi, and P. Radha Krishna. "SeqPAM." In Data Warehousing and Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch208.

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With the growth in the number of Web users and necessity for making information available on the Web, the problem of Web personalization has become very critical and popular. Developers are trying to customize a Web site to the needs of specific users with the help of knowledge acquired from user navigational behavior. Since user page visits are intrinsically sequential in nature, efficient clustering algorithms for sequential data are needed. In this chapter, we introduce a similarity preserving function called sequence and set similarity measure S3M that captures both the order of occurrence of page visits as well as the content of pages. We conducted pilot experiments comparing the results of PAM, a standard clustering algorithm, with two similarity measures: Cosine and S3M. The goodness of the clusters resulting from both the measures was computed using a cluster validation technique based on average levensthein distance. Results on pilot dataset established the effectiveness of S3M for sequential data. Based on these results, we proposed a new clustering algorithm, SeqPAM for clustering sequential data. We tested the new algorithm on two datasets namely, cti and msnbc datasets. We provided recommendations for Web personalization based on the clusters obtained from SeqPAM for msnbc dataset.

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Ali, ABM Shawkat. "K-means Clustering Adopting rbf-Kernel." In Data Mining and Knowledge Discovery Technologies. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-960-1.ch006.

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Clustering technique in data mining has received a significant amount of attention from machine learning community in the last few years as one of the fundamental research area. Among the vast range of clustering algorithm, K-means is one of the most popular clustering algorithm. In this research we extend K-means algorithm by adding well known radial basis function (rbf) kernel and find better performance than classical K-means algorithm. It is a critical issue for rbf kernel, how can we select a unique parameter for optimum clustering task. This present chapter will provide a statistical based solution on this issue. The best parameter selection is considered on the basis of prior information of the data by Maximum Likelihood (ML) method and Nelder-Mead (N-M) simplex method. A rule based meta-learning approach is then proposed for automatic rbf kernel parameter selection.We consider 112 supervised data set and measure the statistical data characteristics using basic statistics, central tendency measure and entropy based approach. We split this data characteristics using well known decision tree approach to generate the rules. Finally we use the generated rules to select the unique parameter value for rbf kernel and then adopt in K-means algorithm. The experiment has been demonstrated with 112 problems and 10 fold cross validation methods. Finally the proposed algorithm can solve any clustering task very quickly with optimum performance.

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Conference papers on the topic "Clustering validation measures"

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Liu, Yanchi, Zhongmou Li, Hui Xiong, Xuedong Gao, and Junjie Wu. "Understanding of Internal Clustering Validation Measures." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.35.

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Xiong, Hui, Junjie Wu, and Jian Chen. "K-means clustering versus validation measures." In the 12th ACM SIGKDD international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1150402.1150503.

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Datta, Susmita, and Somnath Datta. "Validation Measures for Clustering Algorithms Incorporating Biological Information." In 2006 International Multi-Symposiums on Computer and Computational Sciences (IMSCCS). IEEE, 2006. http://dx.doi.org/10.1109/imsccs.2006.139.

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Gupta, Tanvi, and Supriya P. Panda. "Clustering Validation of CLARA and K-Means Using Silhouette & DUNN Measures on Iris Dataset." In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, 2019. http://dx.doi.org/10.1109/comitcon.2019.8862199.

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Xie, Shuyi, Shaohua Dong, and Guangyu Zhang. "Identification of Key Factors of Fire Risk of Oil Depot Based on Fuzzy Clustering Algorithm." In ASME 2019 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/pvp2019-93125.

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Abstract With the rapid development of the national economy, the demand for oil is increasing. In order to meet the increasing energy demand, China has established a number of oil depot in recent years, whose largest capacity reaching up to tens of millions of cubic meters. Due to the flammable and explosive nature of the stored medium, the risk of fire in the oil depot area has increased dramatically as the tank capacity of the storage tank area has increased. The intensification of the oil depot and the development of large-scale oil storage tanks have brought convenience to the national oil

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Naïja, Yosr, and Kaouther Blibech Sinaoui. "A novel measure for validating clustering results applied to road traffic." In the Third International Workshop. ACM Press, 2009. http://dx.doi.org/10.1145/1601966.1601984.

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Haas, Kyle. "Prediction of Structural Reliability Through an Alternative Variability-Based Methodology." In ASME 2019 Verification and Validation Symposium. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/vvs2019-5150.

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Abstract Astonishing increases in computational power have fueled the engineering community’s drive to seek increasingly optimized solutions to structural design problems. Although structural optimization can be critical to achieve a practical and cost-effective design, optimization often comes at a cost to reliability. The competing goals of optimization and reliability amplify the importance of validation, verification, and uncertainty quantification efforts to achieve sufficiently reliable performance. Evaluation of a structural system’s reliability presents a practical challenge to designe

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Kinoshita, Ikuo. "Time Series Clustering and Classification for Uncertainty Analysis by MAAP5 Code." In 2020 International Conference on Nuclear Engineering collocated with the ASME 2020 Power Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icone2020-16437.

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Abstract The MAAP5.04 code uncertainty analysis was carried out for the Power Burst Facility Severe Fuel Damage Test 1-4. Comparisons between experimental data and analysis results were focused on hydrogen generation. The uncertainty propagation analysis was conducted through random variations of input uncertainty parameters of phenomenological models whose ranges were determined by the MAAP5 Zion parameter file. The time series clustering technique using the mean-shift algorithm was applied to the data set generated by the uncertainty propagation analysis. It was confirmed that the code predi

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Brown, Jeffrey M., Joseph Beck, Alexander Kaszynski, and John Clark. "Surrogate Modeling of Manufacturing Variation Effects on Unsteady Interactions in a Transonic Turbine." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76609.

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This effort develops a surrogate modeling approach for predicting the effects of manufacturing variations on blade unsteadiness and performance of a transonic turbine. CFD results from a set of 105 as-manufactured turbine blade geometries are used to train and validate the surrogate models. Blade geometry variation is characterized with point clouds created from a structured light optical measurement system and as-measured CFD grids are generated through mesh morphing of the nominal design grid data. Results from a Reynolds-averaged Navier-Stokes flow solver with the two-equation Wilcox turbul

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Collins, Lance R., Hui Meng, Aruj Ahluwalia, Lujie Cao, and Gang Pan. "Turbulent Coagulation of Aerosol Particles: New Insights From Direct Numerical Simulations and Holographic Imaging Experiments." In ASME/JSME 2003 4th Joint Fluids Summer Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/fedsm2003-45670.

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Particle collisions driven by turbulent fluctuations play a key role in such diverse problems as cloud formation, aerosol powder manufacturing and inhalation drug therapy to name a few. In all of these examples (and many others) turbulent fluctuations increase the rate of collisions relative to the background collision rate driven by Brownian motion. Furthermore, turbulence can spontaneously generate very large fluctuations in the particle concentration field. This “clustering” is caused by the inertial mismatch between the heavy particles and the lighter surrounding gas; vortices in the flow

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