Relevant bibliographies by topics / Statistical cluster analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Statistical cluster analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Statistical cluster analysis"

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KIM, Y. E., M. RABINOWITZ, Y. K. BAE, G. S. CHULICK, and R. A. RICE. "CLUSTER–IMPACT NUCLEAR FUSION: SHOCK–WAVE STATISTICAL ANALYSIS." Modern Physics Letters B 05, no. 14n15 (June 1991): 941–59. http://dx.doi.org/10.1142/s0217984991001179.

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Cluster–impact nuclear fusion is analyzed via a shock–wave model. We show that shock waves can be generated by clusters. Energy loss mechanisms are considered, and the conditions when they are not negligible are determined. Our theoretical model indicates that shock–wave enhanced fusion temperatures are possible with molecular size clusters impacting upon hydrogen isotope targets, somewhat as envisioned by Winterberg and Harrison for macro–projectiles. Our theory explains and reproduces the yields from known target and cluster compositions, as a function of cluster size and energy. Predictions are made, and new tests proposed. We show that contaminants are an unlikely artifact in the experimental data.
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Simioni, M., A. Aparicio, and G. Piotto. "Statistical analysis of Galactic globular cluster type properties." Monthly Notices of the Royal Astronomical Society 495, no. 4 (April 7, 2020): 3981–89. http://dx.doi.org/10.1093/mnras/staa901.

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ABSTRACT The analysis of pseudo-colour diagrams, the so-called chromosome maps, of Galactic globular clusters (GCs) permits to classify them into type I and type II clusters. Type II GCs are characterized by an above-the-average complexity of their chromosome maps and some of them are known to display star-to-star variations of slow neutron-capture reaction elements including iron. This is at the basis of the hypothesis that type II GCs may have an extragalactic origin and were subsequently accreted by the Milky Way. We performed a principal component analysis to explore possible correlations among various GCs parameters in the light of this new classification. The analysis revealed that cluster type correlates mainly with relative age. The cause of this relation was further investigated finding that more metal-rich type II clusters, also appear to be younger and more distant from the Galactic centre. A depletion of type II clusters for positive values of Galactic coordinate Z was also observed, with no type II clusters detected above Z ∼ 2 kpc. Type II cluster orbits also have larger eccentricities than type I ones.
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MORIMOTO, HISAO, and TORU MAEKAWA. "STATISTICAL ANALYSIS OF CLUSTER STRUCTURES FORMED BY DIPOLE-DIPOLE INTERACTIONS." International Journal of Modern Physics B 15, no. 06n07 (March 20, 2001): 912–17. http://dx.doi.org/10.1142/s021797920100543x.

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We developed a statistical model of the cluster formation of ferromagnetic particles and analysed the cluster structures. We investigated the effect of the control parameter λ, that is, the ratio of magnetic dipole moment energy to thermal energy, and external magnetic fields on the fractal dimensions of three-dimensional ferromagnetic clusters. We found that the fractal dimension of clusters, D, changes from 5/3 to 2 as λ increases in the absence of a magnetic field. We also found that when clusters are subjected to a magnetic field, the fractal dimension decreases and the transition region from high fractal dimension to D=1 becomes shorter as λ increases.
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Xu, Dong, and Nancy Redman-Furey. "Statistical cluster analysis of pharmaceutical solvents." International Journal of Pharmaceutics 339, no. 1-2 (July 2007): 175–88. http://dx.doi.org/10.1016/j.ijpharm.2007.03.002.

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Capra, Miranda G. "Factor Analysis of Card Sort Data: An Alternative to Hierarchical Cluster Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 5 (September 2005): 691–95. http://dx.doi.org/10.1177/154193120504900512.

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Software and product designers use card sorting to understand item groups and relationships. In the usability community, a common method of formal statistical analysis for open card sort data is hierarchical cluster analysis, which results in a tree of the items sorted into distinct, nested clusters. Hierarchical cluster analysis is appropriate for highly structured settings, like software menus. However, many situations call for softer clusters, such as designing websites where multiple pages link to the same target page. Factor analysis summarizes the categories created in card sorts and generates clusters that can overlap. This paper explains how to prepare card sort data for statistical analysis, describes the results of factor analysis and how to interpret them, and discusses when hierarchical cluster analysis and factor analysis are appropriate.
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Nemani, Ramya. "Cluster and Factorial Analysis Applications in Statistical Methods." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 5176–82. http://dx.doi.org/10.17762/turcomat.v12i3.2145.

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Cluster analysis is a mathematical technique in Multivariate Data Analysis which indicates the proper guidelines in grouping the data into clusters. We can understand the concept with illustrated notations of cluster Analysis and various Clustering Techniques in this Research paper. Similarity and Dissimilarity measures and Dendogram Analysis will be computed as required measures for Analysis. Factor analysis technique is useful for understanding the underlying hidden factors for the correlations among the variables. Identification and isolation of such facts is sometimes important in several statistical methods in various fields. We can understand the importance of the Factor Analysis and major concept with illustrated Factor Analysis approaches. We can estimated the Basic Factor Modeling and Factor Loadings, and also Factor Rotation process. Provides the complete application process and approaches of Principal Factor M.L.Factor and PCA comparison of Factor Analysis in this Research paper
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Haydar, FMA, NK Paul, and MA Khaleque. "D2 statistical analysis of yield contributing traits in maize (Zea mays L.) inbreds." Bangladesh Journal of Botany 44, no. 4 (October 21, 2018): 629–34. http://dx.doi.org/10.3329/bjb.v44i4.38634.

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Investigation was carried out to determine the genetic divergence in the 25 maize inbred lines. Analysis of variance revealed highly significant differences among all the inbreds. Inbreds were grouped into five clusters, indicating the presence of genetic diversity. The clusters I, IV and V had the highest number of inbreds (6). The maximum inter-cluster distance was observed between clusters I and III (19.279) and the highest intra-cluster distance was recorded in cluster III (0.243) and also wide range of variation was observed in cluster mean performance for the characters studied. Intercrossing among the inbreds belonging to clusters II and III was suggested to develop high yielding inbreds with desirable characters.
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Averbeck, Bruno B., Alexandra Battaglia-Mayer, Carla Guglielmo, and Roberto Caminiti. "Statistical Analysis of Parieto-Frontal Cognitive-Motor Networks." Journal of Neurophysiology 102, no. 3 (September 2009): 1911–20. http://dx.doi.org/10.1152/jn.00519.2009.

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Considerable information has been gathered on the anatomical connectivity within the parieto-frontal network of the primate brain. To examine the statistical regularities in this connectivity, we carried out hierarchical cluster analysis and found statistically significant clusters of areas: four in the parietal and six in the frontal lobe. Clusters were based on patterns of inputs from all cortical areas. Both parietal and frontal clusters were composed of sets of spatially contiguous architectonic areas. The four parietal clusters were composed of sets of anterior (somatosensory), dorsal, inferior, and medio-lateral parietal cortical areas. The six frontal clusters were composed of sets of dorsal premotor, ventral premotor, primary motor, cingulate motor, and dorsal and ventral prefrontal cortical areas. Furthermore, connectivity between frontal and parietal clusters was topographic and reciprocal. Thus we found substantial statistical structure and organization in the parieto-frontal network that gives a simplified but accurate description of this system.
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Ryan, Catherine J., Karen M. Vuckovic, Lorna Finnegan, Chang G. Park, Lani Zimmerman, Bunny Pozehl, Paula Schulz, Susan Barnason, and Holli A. DeVon. "Acute Coronary Syndrome Symptom Clusters: Illustration of Results Using Multiple Statistical Methods." Western Journal of Nursing Research 41, no. 7 (January 22, 2019): 1032–55. http://dx.doi.org/10.1177/0193945918822323.

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Researchers have employed various methods to identify symptom clusters in cardiovascular conditions, without identifying rationale. Here, we test clustering techniques and outcomes using a data set from patients with acute coronary syndrome. A total of 474 patients who presented to emergency departments in five United States regions were enrolled. Symptoms were assessed within 15 min of presentation using the validated 13-item ACS Symptom Checklist. Three variable-centered approaches resulted in four-factor solutions. Two of three person-centered approaches resulted in three-cluster solutions. K-means cluster analysis revealed a six-cluster solution but was reduced to three clusters following cluster plot analysis. The number of symptoms and patient characteristics varied within clusters. Based on our findings, we recommend using (a) a variable-centered approach if the research is exploratory, (b) a confirmatory factor analysis if there is a hypothesis about symptom clusters, and (c) a person-centered approach if the aim is to cluster symptoms by individual groups.
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Kim, Ji Ye, and Eun Hye Ha. "Cluster Analysis of the Child Behavior Checklist 1.5–5 for Preschool Children Diagnosed With a Mental Disorder." Psychological Reports 123, no. 4 (May 2, 2019): 1403–24. http://dx.doi.org/10.1177/0033294119844980.

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Research on the relationship between the Child Behavior Checklist (CBCL) and Diagnostic and Statistical Manual for Mental Disorder diagnoses for preschool children is scarce. Cluster analysis can be useful for investigating characteristics of a clinical group by using CBCL subscales and classifying subtypes of a Diagnostic and Statistical Manual for Mental Disorder diagnosis group. This study conducted a cluster analysis of the CBCL 1.5–5 for preschool children diagnosed with a mental disorder. Participants were 333 children (255 males and 78 females) aged 1.5 to 5 years who were diagnosed with a mental disorder. The CBCL 1.5–5 and Bayley Scales of Infant Development II were used as assessment instruments. Three clusters were extracted and then compared with CBCL 1.5–5 profiles of each Diagnostic and Statistical Manual for Mental Disorder-5 subject group to determine their clusters. Cluster 1 was named “intellectual disorder cluster.” Cluster 2 was referred to as “other mental disorders cluster,” and Cluster 3 was called “autism spectrum disorder cluster.” When differences in profiles of behavior problems distinguished by CBCL 1.5–5 scales were examined among different clusters, discriminant validity was found to be high.
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Dissertations / Theses on the topic "Statistical cluster analysis"

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Sullivan, Terry. "The Cluster Hypothesis: A Visual/Statistical Analysis." Thesis, University of North Texas, 2000. https://digital.library.unt.edu/ark:/67531/metadc2444/.

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By allowing judgments based on a small number of exemplar documents to be applied to a larger number of unexamined documents, clustered presentation of search results represents an intuitively attractive possibility for reducing the cognitive resource demands on human users of information retrieval systems. However, clustered presentation of search results is sensible only to the extent that naturally occurring similarity relationships among documents correspond to topically coherent clusters. The Cluster Hypothesis posits just such a systematic relationship between document similarity and topical relevance. To date, experimental validation of the Cluster Hypothesis has proved problematic, with collection-specific results both supporting and failing to support this fundamental theoretical postulate. The present study consists of two computational information visualization experiments, representing a two-tiered test of the Cluster Hypothesis under adverse conditions. Both experiments rely on multidimensionally scaled representations of interdocument similarity matrices. Experiment 1 is a term-reduction condition, in which descriptive titles are extracted from Associated Press news stories drawn from the TREC information retrieval test collection. The clustering behavior of these titles is compared to the behavior of the corresponding full text via statistical analysis of the visual characteristics of a two-dimensional similarity map. Experiment 2 is a dimensionality reduction condition, in which inter-item similarity coefficients for full text documents are scaled into a single dimension and then rendered as a two-dimensional visualization; the clustering behavior of relevant documents within these unidimensionally scaled representations is examined via visual and statistical methods. Taken as a whole, results of both experiments lend strong though not unqualified support to the Cluster Hypothesis. In Experiment 1, semantically meaningful 6.6-word document surrogates systematically conform to the predictions of the Cluster Hypothesis. In Experiment 2, the majority of the unidimensionally scaled datasets exhibit a marked nonuniformity of distribution of relevant documents, further supporting the Cluster Hypothesis. Results of the two experiments are profoundly question-specific. Post hoc analyses suggest that it may be possible to predict the success of clustered searching based on the lexical characteristics of users' natural-language expression of their information need.
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Santiago, Calderón José Bayoán. "On Cluster Robust Models." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cgu_etd/132.

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Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, the average treatment effect is a function of the various partial treatment effects and the composition of the population of interest. The first chapter explores the performance of common estimators as a function of the presence of heterogeneity in treatment effects and other characteristics that may influence their performance for estimating average treatment effects. The second chapter examines various approaches to evaluating and improving cluster structures as a way to obtain cluster-robust models. Both chapters are intended to be useful to practitioners as a how-to guide to examine and think about their applications and relevant factors. Empirical examples are provided to illustrate theoretical results, showcase potential tools, and communicate a suggested thought process. The third chapter relates to an open-source statistical software package for the Julia language. The content includes a description for the software functionality and technical elements. In addition, it features a critique and suggestions for statistical software development and the Julia ecosystem. These comments come from my experience throughout the development process of the package and related activities as an open-source and professional software developer. One goal of the paper is to make econometrics more accessible not only through accessibility to functionality, but understanding of the code, mathematics, and transparency in implementations.
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Hager, Creighton Tsuan-Ren. "Statistical Analysis of ATM Call Detail Records." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/30937.

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Network management is a problem that faces designers and operators of any type of network. Conventional methods of capacity planning or configuration management are difficult to apply directly to networks that dynamically allocate resources, such as Asynchronous Transfer Mode (ATM) networks and emerging Internet Protocol (IP) networks employing Differentiated Services (DiffServ). This work shows a method to generically classify traffic in an ATM network such that capacity planning may be possible. These methods are generally applicable to other networks that support dynamically allocated resources. In this research, Call Detail Records (CDRs) captured from a ¡§live¡¨ ATM network were successfully classified into three traffic categories. The traffic categories correspond to three different video speeds (1152 kbps, 768 kbps, and 384 kbps) in the network. Further statistical analysis was used to characterize these traffic categories and found them to fit deterministic distributions. The statistical analysis methods were also applied to several different network planning and management functions. Three specific potential applications related to network management were examined: capacity planning, traffic modeling, and configuration management.
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Vohra, Neeru Rani. "Three dimensional statistical graphs, visual cues and clustering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ56213.pdf.

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Chung, Hyoju. "GEE with large cluster sizes : high-dimensional working correlation models /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/9545.

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Farahi, Arya, August E. Evrard, Eduardo Rozo, Eli S. Rykoff, and Risa H. Wechsler. "Galaxy cluster mass estimation from stacked spectroscopic analysis." OXFORD UNIV PRESS, 2016. http://hdl.handle.net/10150/621426.

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We use simulated galaxy surveys to study: (i) how galaxy membership in redMaPPer clusters maps to the underlying halo population, and (ii) the accuracy of a mean dynamical cluster mass, M-sigma(lambda), derived from stacked pairwise spectroscopy of clusters with richness lambda. Using similar to 130 000 galaxy pairs patterned after the Sloan Digital Sky Survey (SDSS) redMaPPer cluster sample study of Rozo et al., we show that the pairwise velocity probability density function of central-satellite pairs with m(i) < 19 in the simulation matches the form seen in Rozo et al. Through joint membership matching, we deconstruct the main Gaussian velocity component into its halo contributions, finding that the top-ranked halo contributes similar to 60 per cent of the stacked signal. The halo mass scale inferred by applying the virial scaling of Evrard et al. to the velocity normalization matches, to within a few per cent, the log-mean halo mass derived through galaxy membership matching. We apply this approach, along with miscentring and galaxy velocity bias corrections, to estimate the log-mean matched halo mass at z = 0.2 of SDSS redMaPPer clusters. Employing the velocity bias constraints of Guo et al., we find aEuroln (M-200c)|lambda aEuro parts per thousand = ln (< M-30) + alpha(m) ln (lambda/30) with M-30 = 1.56 +/- 0.35 x 10(14) M-aS (TM) and alpha(m) = 1.31 +/- 0.06(stat) +/- 0.13(sys). Systematic uncertainty in the velocity bias of satellite galaxies overwhelmingly dominates the error budget.
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Gomes, Manuel. "Statistical methods for cost-effectiveness analysis that use cluster-randomised trials." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2012. http://researchonline.lshtm.ac.uk/4646546/.

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This thesis considers alternative statistical methods for cost-effectiveness analysis (CEA) that use cluster randomised trials (CRTs). The thesis has four objectives: firstly to develop criteria for identifying appropriate methods for CEA that use CRTs; secondly to critically appraise the methods used in applied CEAs that use CRTs; thirdly to assess the performance of alternative methods for CEA that use CRTs in settings where baseline covariates are balanced; fourthly to compare statistical methods that adjust for systematic covariate imbalance in CEA that use CRTs. The thesis developed a checklist to assess the methodological quality of published CEAs that use CRTs. This checklist was informed by a conceptual review of statistical methods, and applied in a systematic literature review of published CEAs that use CRTs. The review found that most studies adopted statistical methods that ignored clustering or correlation between costs and health outcomes. A simulation study was conducted to assess the performance of alternative methods for CEA that use CRTs across different circumstances where baseline covariates are balanced. This study considered: seemingly unrelated regression (SUR) and generalised estimating equations (GEEs), both with a robust standard error; multilevel models (MLMs) and a non-parametric 'two-stage' bootstrap (TS8). Performance was reported as, for example, bias and confidence interval (Cl) coverage of the incremental net benefit. The MLMs and the TSB performed well across all settings; SUR and GEEs reported poor Cl coverage in CRTs with few clusters. The thesis compared methods for CEA that use CRTs when there are systematic differences in baseline covariates between the treatment groups. In a case study and further simulations, the thesis considered SUR, MLMs, and TSB combined with SUR to adjust for covariate imbalance. The case-study showed that cost-effectiveness results can differ according to adjustment method. The simulations reported that MLMs performed well across all settings, and unlike the other methods, provided Cl coverage close to nominal levels, even with few clusters and unequal cluster sizes. The thesis concludes that MLMs are the most appropriate method across the circumstances considered. This thesis presents methods for improving the quality ofCEA that use CRTs, to help future studies provide a sound basis for policy making.
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Majeed, Salar Mustafa. "Cluster detection and analysis with geo-spatial datasets using a hybrid statistical and neural networks hierarchical approach." Thesis, University of South Wales, 2010. https://pure.southwales.ac.uk/en/studentthesis/cluster-detection-and-analysis-with-geospatial-datasets-using-a-hybrid-statistical-and-neural-networks-hierarchical-approach(c57662b9-b685-4cfb-bd04-33e6e1655758).html.

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Spatial datasets contain information relating to the locations of incidents of phenomena for example, crime and disease. Areas that contain a higher than expected incidence of the phenomena, given background population and census datasets, are of particular interest. By analysing the locations of potential influence, it may be possible to establish where a cause and effect relationship is present in the observed process. Cluster detection techniques can be applied to such datasets in order to reveal information relating to the spatial distribution of the cases. Research in these areas has mainly concentrated on either computational or statistical aspects of cluster detection. Each clustering algorithm has its own strengths and weakness. Their main weaknesses causing their unreliability can be estimating the number of clusters, testing the number of components, selecting initial seeds (centroids), running time and memory requirements. Consequently, a new cluster detection methodology has been developed in this thesis based on knowledge drawn from both statistical and computing domains. This methodology is based on a hybrid of statistical methods using properties of probability rather than distance to associate data with clusters. No previous knowledge of the dataset is required and the number of clusters is not predetermined. It performs efficiently in terms of memory requirements, running time and cluster quality. The algorithm for determining both the centre of clusters and the existence of the clusters themselves was applied and tested on simulated and real datasets. The results which were obtained from identification of hotspots were compared with results of other available algorithms such as CLAP (Cluster Location Analysis Procedure), Satscan and GAM (Geographical Analysis Machine). The outputs are very similar. XVI GIS presented in this thesis encompasses the SCS algorithm, statistics and neural networks for developing a hybrid predictive crime model, mapping, visualizing crime data and the corresponding population in the study region, visualizing the location of obtained clusters and burglary incidence concentration ‘hotspots’ which was specified by clustering algorithm SCS. Naturally the quality of results is subject to the accuracy of the used data. GIS is used in this thesis for developing a methodology for modelling data containing multiple functions. The census data used throughout this construction provided a useful source of geo-demographic information. The obtained datasets were used for predictive crime modelling. This thesis has benefited from several existing methodologies to develop a hybrid modelling approach. The methodology was applied to real data on burglary incidence distribution in the study region. Relevant principles of statistics, Geographical Information System, Neural Networks and SCS algorithm were utilized for the analysis of observed data. Regression analysis was used for building a predictive crime model and combined with Neural Networks with the aim of developing a new hierarchical neural Network approaches to generate a more reliable prediction. The promising results were compared with the non-hierarchical neural Network back-propagation network and multiple regression analysis. The average percentage accuracy achieved by the new methodology at testing stage increase 13% compared with the non-hierarchical BP performance. In general the analysis reveals a number of predictors that increase the risk of burglary in the study region. Specifically living in a household in which there is ‘one person’, ‘lone parent’, household where occupations are in elementary or intermediate and unemployed. For the influence of Household space, the results indicate that the risk of burglary rate increases within the household living in shared houses.
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Fiero, Mallorie H. "Statistical Approaches for Handling Missing Data in Cluster Randomized Trials." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612860.

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In cluster randomized trials (CRTs), groups of participants are randomized as opposed to individual participants. This design is often chosen to minimize treatment arm contamination or to enhance compliance among participants. In CRTs, we cannot assume independence among individuals within the same cluster because of their similarity, which leads to decreased statistical power compared to individually randomized trials. The intracluster correlation coefficient (ICC) is crucial in the design and analysis of CRTs, and measures the proportion of total variance due to clustering. Missing data is a common problem in CRTs and should be accommodated with appropriate statistical techniques because they can compromise the advantages created by randomization and are a potential source of bias. In three papers, I investigate statistical approaches for handling missing data in CRTs. In the first paper, I carry out a systematic review evaluating current practice of handling missing data in CRTs. The results show high rates of missing data in the majority of CRTs, yet handling of missing data remains suboptimal. Fourteen (16%) of the 86 reviewed trials reported carrying out a sensitivity analysis for missing data. Despite suggestions to weaken the missing data assumption from the primary analysis, only five of the trials weakened the assumption. None of the trials reported using missing not at random (MNAR) models. Due to the low proportion of CRTs reporting an appropriate sensitivity analysis for missing data, the second paper aims to facilitate performing a sensitivity analysis for missing data in CRTs by extending the pattern mixture approach for missing clustered data under the MNAR assumption. I implement multilevel multiple imputation (MI) in order to account for the hierarchical structure found in CRTs, and multiply imputed values by a sensitivity parameter, k, to examine parameters of interest under different missing data assumptions. The simulation results show that estimates of parameters of interest in CRTs can vary widely under different missing data assumptions. A high proportion of missing data can occur among CRTs because missing data can be found at the individual level as well as the cluster level. In the third paper, I use a simulation study to compare missing data strategies to handle missing cluster level covariates, including the linear mixed effects model, single imputation, single level MI ignoring clustering, MI incorporating clusters as fixed effects, and MI at the cluster level using aggregated data. The results show that when the ICC is small (ICC ≤ 0.1) and the proportion of missing data is low (≤ 25\%), the mixed model generates unbiased estimates of regression coefficients and ICC. When the ICC is higher (ICC > 0.1), MI at the cluster level using aggregated data performs well for missing cluster level covariates, though caution should be taken if the percentage of missing data is high.
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Torp, Emil, and Patrik Önnegren. "Driving Cycle Generation Using Statistical Analysis and Markov Chains." Thesis, Linköpings universitet, Fordonssystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94147.

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A driving cycle is a velocity profile over time. Driving cycles can be used for environmental classification of cars and to evaluate vehicle performance. The benefit by using stochastic driving cycles instead of predefined driving cycles, i.e. the New European Driving Cycle, is for instance that the risk of cycle beating is reduced. Different methods to generate stochastic driving cycles based on real-world data have been used around the world, but the representativeness of the generated driving cycles has been difficult to ensure. The possibility to generate stochastic driving cycles that captures specific features from a set of real-world driving cycles is studied. Data from more than 500 real-world trips has been processed and categorized. The driving cycles are merged into several transition probability matrices (TPMs), where each element corresponds to a specific state defined by its velocity and acceleration. The TPMs are used with Markov chain theory to generate stochastic driving cycles. The driving cycles are validated using percentile limits on a set of characteristic variables, that are obtained from statistical analysis of real-world driving cycles. The distribution of the generated driving cycles is investigated and compared to real-world driving cycles distribution. The generated driving cycles proves to represent the original set of real-world driving cycles in terms of key variables determined through statistical analysis. Four different methods are used to determine which statistical variables that describes the features of the provided driving cycles. Two of the methods uses regression analysis. Hierarchical clustering of statistical variables is proposed as a third alternative, and the last method combines the cluster analysis with the regression analysis. The entire process is automated and a graphical user interface is developed in Matlab to facilitate the use of the software.
En körcykel är en beskriving av hur hastigheten för ett fordon ändras under en körning. Körcykler används bland annat till att miljöklassa bilar och för att utvärdera fordonsprestanda. Olika metoder för att generera stokastiska körcykler baserade på verklig data har använts runt om i världen, men det har varit svårt att efterlikna naturliga körcykler. Möjligheten att generera stokastiska körcykler som representerar en uppsättning naturliga körcykler studeras. Data från över 500 körcykler bearbetas och kategoriseras. Dessa används för att skapa överergångsmatriser där varje element motsvarar ett visst tillstånd, med hastighet och acceleration som tillståndsvariabler. Matrisen tillsammans med teorin om Markovkedjor används för att generera stokastiska körcykler. De genererade körcyklerna valideras med hjälp percentilgränser för ett antal karaktäristiska variabler som beräknats för de naturliga körcyklerna. Hastighets- och accelerationsfördelningen hos de genererade körcyklerna studeras och jämförs med de naturliga körcyklerna för att säkerställa att de är representativa. Statistiska egenskaper jämfördes och de genererade körcyklerna visade sig likna den ursprungliga uppsättningen körcykler. Fyra olika metoder används för att bestämma vilka statistiska variabler som beskriver de naturliga körcyklerna. Två av metoderna använder regressionsanalys. Hierarkisk klustring av statistiska variabler föreslås som ett tredje alternativ. Den sista metoden kombinerar klusteranalysen med regressionsanalysen. Hela processen är automatiserad och ett grafiskt användargränssnitt har utvecklats i Matlab för att underlätta användningen av programmet.
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Books on the topic "Statistical cluster analysis"

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McEwan, J. A. Cluster analysis and preference mapping. Chipping Campden: Campden & Chorleywood Food Research Association, 1998.

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Statistical methods for disease clustering. New York: Springer, 2010.

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J, Hayes Richard, and Hayes Richard J. Cluster randomised trials. Boca Raton: Taylor & Francis, 2009.

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D, Nagel, and Sator H, eds. Cluster analysis in clinical chemistry: A model. Chichester: Wiley, 1987.

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Brown, Jennifer A. The relative efficiency of adaptive cluster sampling for ecological surveys. Palmerston North, N.Z: Faculty of Information and Mathematical Sciences, Massey University, 1996.

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Canada, Canada Agriculture, ed. Optimal set covering for biological classification. Ottawa: Agriculture Canada, 1993.

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Stahl, Herbert. Clusteranalyse grosser Objektmengen mit problemorientierten Distanzmassen. Thun: H. Deutsch, 1985.

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Das Problem der Distanzbindungen in der hierarchischen Clusteranalyse. Frankfurt am Main: P. Lang, 1995.

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Dimpsey, Robert Tod. Performance analysis of the Alliant FX/8 multiprocessor using statistical clustering. Urbana, IL: University of Illinois at Urbana-Champaign, 1988.

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Neil, Klar, ed. Design and analysis of cluster randomization trials in health research. London: Arnold, 2000.

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Book chapters on the topic "Statistical cluster analysis"

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Baragona, Roberto, Francesco Battaglia, and Irene Poli. "Cluster Analysis." In Evolutionary Statistical Procedures, 199–260. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16218-3_7.

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Härdle, Wolfgang Karl, and Léopold Simar. "Cluster Analysis." In Applied Multivariate Statistical Analysis, 363–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26006-4_13.

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Härdle, Wolfgang Karl, and Léopold Simar. "Cluster Analysis." In Applied Multivariate Statistical Analysis, 385–405. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_13.

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Härdle, Wolfgang Karl, and Léopold Simar. "Cluster Analysis." In Applied Multivariate Statistical Analysis, 331–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17229-8_12.

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Härdle, Wolfgang, and Léopold Simar. "Cluster Analysis." In Applied Multivariate Statistical Analysis, 301–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_11.

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Gatignon, Hubert. "Cluster Analysis." In Statistical Analysis of Management Data, 295–322. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1270-1_11.

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Gatignon, Hubert. "Cluster Analysis." In Statistical Analysis of Management Data, 453–85. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-8594-0_12.

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Reddy, M. Venkataswamy. "Cluster Analysis." In Statistical Methods in Psychiatry Research and SPSS, 209–60. Second edition. | Toronto ; New Jersey : Apple Academic Press, 2018.: Apple Academic Press, 2019. http://dx.doi.org/10.1201/9780429023309-16.

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Falk, Michael, Frank Marohn, and Bernward Tewes. "Cluster Analysis." In Foundations of Statistical Analyses and Applications with SAS, 271–319. Basel: Birkhäuser Basel, 1995. http://dx.doi.org/10.1007/978-3-0348-8195-1_7.

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Romesburg, H. Charles. "Cluster Analysis: An Introduction." In International Encyclopedia of Statistical Science, 262–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_310.

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Conference papers on the topic "Statistical cluster analysis"

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Liu, Jialin, and Yong Chen. "Fast data analysis with integrated statistical metadata in scientific datasets." In 2013 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2013. http://dx.doi.org/10.1109/cluster.2013.6702623.

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Brandt, J. M., A. C. Gentile, Y. M. Marzouk, and P. P. Pebay. "Meaningful Automated Statistical Analysis of Large Computational Clusters." In 2005 IEEE International Conference on Cluster Computing. IEEE, 2005. http://dx.doi.org/10.1109/clustr.2005.347090.

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Carino, Ricolindo L., and Ioana Banicescu. "A Framework for Statistical Analysis of Datasets on Heterogeneous Clusters." In 2005 IEEE International Conference on Cluster Computing. IEEE, 2005. http://dx.doi.org/10.1109/clustr.2005.347019.

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Butters, T. D., T. J. Sharpe, S. Guttel, and J. L. Shapiro. "Statistical cluster analysis and visualisation for alarm management configuration." In Asset Management Conference 2014. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.1027.

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Ferreira, Leonardo, Estela Ribeiro, and Carlos Thomaz. "A cluster analysis of benchmark acoustic features on Brazilian music." In Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbcm.2019.10444.

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In this work, we extend a standard and successful acoustic feature extraction approach based on trigger selection to examples of Brazilian Bossa-Nova and Heitor Villa Lobos music pieces. Additionally, we propose and implement a computational framework to disclose whether all the acoustic features extracted are statistically relevant, that is, non-redundant. Our experimental results show that not all these well-known features might be necessary for trigger selection, given the multivariate statistical redundancy found, which associated all these acoustic features into 3 clusters with different factor loadings and, consequently, representatives.
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Brandt, Jim, Ann Gentile, Jackson Mayo, Philippe Pébay, Diana Roe, David Thompson, and Matthew Wong. "Methodologies for advance warning of compute cluster problems via statistical analysis." In the 2009 workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1552526.1552528.

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Farjad, Sheikh Muhammad, and Asad Arfeen. "Cluster Analysis and Statistical Modeling: A Unified Approach for Packet Inspection." In 2020 International Conference on Cyber Warfare and Security (ICCWS). IEEE, 2020. http://dx.doi.org/10.1109/iccws48432.2020.9292396.

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Sunaga, Daniele Yumi, Julio Cesar Nievola, and Milton Pires Ramos. "Statistical and Biological Validation Methods in Cluster Analysis of Gene Expression." In 2007 International Conference on Machine Learning and Applications. IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.55.

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Erilli, Necati Alp, and Çağatay Karaköy. "Classification of Turkish Republics with Specific Economic Indicators in Fuzzy Clustering Analysis." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01253.

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Economic indicators in economic policies have an important place in determining the levels of development. Determining and classifying the existing social and economic structures of countries is very important for examining the development states and possible development tendencies of countries and forming regional development policies in line with these. The aim in cluster analysis, is to classify datas in to similarity and perform useful knowledge for the researcher. Cluster analysis, which became more popular among the subjects of statistical classification in recent years, can give more reliable results when there is apriori knowledge about number of clusters. Fuzzy models interested in fuzzy model structures and try to estimate system behaviours that has no knowledge about their structure. Fuzzy Cluster Analysis is try to decompose the groups which membership degrees cannot be determined. When the number of datas and variables increased or cluster structures came to closer for all, Cluster analysis has given more successful results then the other cluster analysis methods. In this study, Turkish Republics were classified in terms of the indicators determined by using Fuzzy C-Means (FCM) and Gath Geva methods which are frequently used in fuzzy clustering analysis. The objective was to find out the common class structures of Turkish Republics which came out with the disintegration of the Soviet Union in 1991 and which experienced economic similar problems and thus to help countries in the same clusters in similar economic planning. Results are also compared between fuzzy and crisp clustering analysis methods.
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Carmo, Marcus Fabio Fontenelle do, Gabriel Paulino Siqueira Junior, Jose Everardo Bessa Maia, Raimir Holanda, and Jose Neuman de Sousa. "Using Statistical Discriminators and Cluster Analysis to P2P and Attack Traffic Monitoring." In 2007 Latin American Network Operations and Management Symposium. IEEE, 2007. http://dx.doi.org/10.1109/lanoms.2007.4362461.

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Reports on the topic "Statistical cluster analysis"

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Sclove, Stanley L. Statistical Models and Methods for Cluster Analysis and Image Segmentation. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada169145.

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Gentile, Ann C., Youssef M. Marzouk, James M. Brandt, and Philippe Pierre Pebay. Meaningful statistical analysis of large computational clusters. Office of Scientific and Technical Information (OSTI), July 2005. http://dx.doi.org/10.2172/958384.

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Kumar, Indraneel, Lionel Beaulieu, Annie Cruz-Porter, Chun Song, Benjamin St. Germain, and Andrey Zhalnin. An Assessment of the Workforce and Occupations in the Highway, Street, and Bridge Construction Industries in Indiana. Purdue University, 2020. http://dx.doi.org/10.5703/1288284315018.

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This project explores workforce and occupations within the highway, street, and bridge construction industries (NAICS 237310) in Indiana. There are five specific deliverable comprised of three data reports, one policy document, and a website. The first data report includes an assessment of the workforce based on the eight-part framework, which are industry, occupations, job postings, hard-to-fill jobs, Classification of Instructional Programs (CIP), GAP Analysis, compatibility, and automation. The report defines a cluster followed by a detailed analysis of the occupations, skills, job postings, etc., in the NAICS 237310 industry in Indiana. The report makes use of specialized labor market databases, such as the Economic Modeling Specialists International (EMSI), CHMURA JobsEQ, etc. The analysis is based only on the jobs covered under the unemployment insurance or the Quarterly Census of Employment and Wages (QCEW) data. The second data report analyzes jobs to jobs flows to and from the construction industry in Indiana, with a particular emphasis on the Great Recession, by utilizing the Bureau of Labor Statistics (BLS) data. The third data report looks into the equal employment opportunity or Section 1391 and 1392 data for Indiana and analyzes specific characteristics of that data. The policy report includes a set of recommendations for workforce development for INDOT and a summary of the three data reports. The key data on occupations within the NAICS 237310 are provided in an interactive website. The website provides a data dashboard for individual INDOT Districts. The policy document recommends steps for development of the highways, streets and bridges construction workforce in INDOT Districts.
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McCarthy, Noel, Eileen Taylor, Martin Maiden, Alison Cody, Melissa Jansen van Rensburg, Margaret Varga, Sophie Hedges, et al. Enhanced molecular-based (MLST/whole genome) surveillance and source attribution of Campylobacter infections in the UK. Food Standards Agency, July 2021. http://dx.doi.org/10.46756/sci.fsa.ksj135.

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This human campylobacteriosis sentinel surveillance project was based at two sites in Oxfordshire and North East England chosen (i) to be representative of the English population on the Office for National Statistics urban-rural classification and (ii) to provide continuity with genetic surveillance started in Oxfordshire in October 2003. Between October 2015 and September 2018 epidemiological questionnaires and genome sequencing of isolates from human cases was accompanied by sampling and genome sequencing of isolates from possible food animal sources. The principal aim was to estimate the contributions of the main sources of human infection and to identify any changes over time. An extension to the project focussed on antimicrobial resistance in study isolates and older archived isolates. These older isolates were from earlier years at the Oxfordshire site and the earliest available coherent set of isolates from the national archive at Public Health England (1997/8). The aim of this additional work was to analyse the emergence of the antimicrobial resistance that is now present among human isolates and to describe and compare antimicrobial resistance in recent food animal isolates. Having identified the presence of bias in population genetic attribution, and that this was not addressed in the published literature, this study developed an approach to adjust for bias in population genetic attribution, and an alternative approach to attribution using sentinel types. Using these approaches the study estimated that approximately 70% of Campylobacter jejuni and just under 50% of C. coli infection in our sample was linked to the chicken source and that this was relatively stable over time. Ruminants were identified as the second most common source for C. jejuni and the most common for C. coli where there was also some evidence for pig as a source although less common than ruminant or chicken. These genomic attributions of themselves make no inference on routes of transmission. However, those infected with isolates genetically typical of chicken origin were substantially more likely to have eaten chicken than those infected with ruminant types. Consumption of lamb’s liver was very strongly associated with infection by a strain genetically typical of a ruminant source. These findings support consumption of these foods as being important in the transmission of these infections and highlight a potentially important role for lamb’s liver consumption as a source of Campylobacter infection. Antimicrobial resistance was predicted from genomic data using a pipeline validated by Public Health England and using BIGSdb software. In C. jejuni this showed a nine-fold increase in resistance to fluoroquinolones from 1997 to 2018. Tetracycline resistance was also common, with higher initial resistance (1997) and less substantial change over time. Resistance to aminoglycosides or macrolides remained low in human cases across all time periods. Among C. jejuni food animal isolates, fluoroquinolone resistance was common among isolates from chicken and substantially less common among ruminants, ducks or pigs. Tetracycline resistance was common across chicken, duck and pig but lower among ruminant origin isolates. In C. coli resistance to all four antimicrobial classes rose from low levels in 1997. The fluoroquinolone rise appears to have levelled off earlier and among animals, levels are high in duck as well as chicken isolates, although based on small sample sizes, macrolide and aminoglycoside resistance, was substantially higher than for C. jejuni among humans and highest among pig origin isolates. Tetracycline resistance is high in isolates from pigs and the very small sample from ducks. Antibiotic use following diagnosis was relatively high (43.4%) among respondents in the human surveillance study. Moreover, it varied substantially across sites and was highest among non-elderly adults compared to older adults or children suggesting opportunities for improved antimicrobial stewardship. The study also found evidence for stable lineages over time across human and source animal species as well as some tighter genomic clusters that may represent outbreaks. The genomic dataset will allow extensive further work beyond the specific goals of the study. This has been made accessible on the web, with access supported by data visualisation tools.
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