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PROJECTION PURSUIT exploratory data-analysis

COMPUTATIONAL STATISTICS & data analysis 1995年第6期20卷 669-687页

作者： POSSE, C STANFORD UNIV DEPT STATSTANFORDCA 94305

Posse (1990) presented a projection pursuit technique, based on a global optimization algorithm and on a chi-squared projection index, for finding the plane in which the data are the most interesting. This paper extends and improves this algorithm providing an exploratory data analysis by projection pursuit that has important advantages over its competitors. The global optimization algorithm, when combined with a structure removal procedure due to Friedman (1987), allows a sequential identification of interesting bidimensional views of decreasing importance. The modified chi-squared index satisfies the five basic demands for a projection index. It is (1) uniquely minimized at the bivariate normal distribution, (2) approximately affine invariant, (3) consistent, (4) resistant to features in the tail of the distribution and, (5) simple enough to permit quick computation even for large data sets. The paper gives simple rules for judging the significance of a structure found by this algorithm. These rules define a stopping criterion for the search process. They are based on theoretical (asymptotic) arguments and are well-supported by simulations. The efficacy of the new algorithm is illustrated through several studies of real and simulated data.

关键词： CLUSTER analysis exploratory data analysis GLOBAL OPTIMIZATION INVARIANT CHI-SQUARED TEST STATISTICS P-VALUES FOR PROJECTIONS PROJECTION PURSUIT 2-DIMENSIONAL PROJECTIONS

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Multivariate exploratory data analysis (MEDA) Toolbox for Matlab

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2015年 143卷 49-57页

作者： Camacho, Jose Perez-Villegas, Alejandro Rodriguez-Gomez, Rafael A. Jimenez-Manas, Elena Univ Granada Dept Teoria Senal Telemat & Comunicac E-18071 Granada Spain

The Multivariate exploratory data analysis (MEDA) Toolbox in Matlab is a set of multivariate analysis tools for the exploration of data sets. In the MEDA Toolbox, traditional exploratory plots based on Principal Component analysis (PCA) or Partial Least Squares (PIS), such as score, loading and residual plots, are combined with new methods like MEDA, MEDA and SVI plots. The latter are aimed at solving some of the limitations found in the former to adequately extract conclusions from a data set. Also, other useful tools such as cross-validation algorithms, Multivariate Statistical Process Control (MSPC) charts and data simulation/approximation algorithms (ADICOV) are included in the toolbox. Finally, most of the exploratory tools are extended for their use with very large data sets (Big data), with unlimited number of observations. (C) 2015 Elsevier B.V. All rights reserved.

关键词： exploratory data analysis Software Matlab Principal Component analysis Partial Least Squares Big data

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Student Performance Patterns in Engineering at the University of Johannesburg: An exploratory data analysis

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IEEE ACCESS 2023年 11卷 48977-48987页

作者： Maphosa, Mfowabo Doorsamy, Wesley Paul, Babu S. Univ Johannesburg Inst Intelligent Syst ZA-2006 Johannesburg South Africa Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT England

Globally, the increased demand for engineers is not matched by an increase in graduates. This is further exacerbated by the fact that student dropout rates in engineering are higher than in other disciplines. Understanding engineering students' performance patterns and potential influences can lead to developing interventions to improve engineering students' success. Recent advances in data science and educational data mining have made it possible to extract valuable information from historical data, which can supplement interventions. This study sought to extract insights and information from real-world data, analyse correlations in the dataset's variables and better understand the influences of student performance. exploratory data analysis was applied to the dataset to visualise the dataset and infer the correlations between variables provided in the dataset on student performance patterns. We used Python for data analysis and visualising the correlation between variables. The results show gender disparity in engineering enrollments, with only a quarter of female students enrolled. The study also indicates that the completion rates could be much higher. Another finding is that most students who drop out do so because of choosing the wrong qualifications. Furthermore, when comparing the percentages, female students performed slightly better than their male counterparts. The correlation analysis shows no relationships between gender, race, admission point score, mathematics marks and science marks with student performance in engineering. Understanding student performance patterns can reduce dropout rates by correctly advising students to enrol on the most suitable programmes, and aid support interventions are needed to improve student success in engineering.

关键词： Engineering education exploratory data analysis extended programmes mainstream programmes student performance

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On the application of exploratory data analysis for characterization of space weather data sets

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ADVANCES IN SPACE RESEARCH 2011年第12期47卷 2199-2209页

作者： Krause, L. Habash Franz, A. Stevenson, A. NASA Marshall Spaceflight Ctr Space Sci Off Marshall Space Flight Ctr 4207 Huntsville AL 35808 USA USAF Acad Dept Phys Colorado Springs CO 80840 USA

This paper presents an overview of the mathematical foundations for techniques in exploratory data analysis (EDA) for the purpose of investigating the relationships among the numerous variables in large sets of multivariate space weather data. Specifically, we cover techniques in Principal Components analysis (PCA) and Common Factor analysis (CFA). These techniques are illustrated using space weather activity indices collected during the year 2002 and the corresponding noon-time hmF2 data from the International Reference Ionosphere (IRI). A CFA is used to categorize the activity indices, and a PCA is used to derive two macro-indices of activity to ascertain the strength of solar and geomagnetic activity. These macro-indices are then used to compare and contrast IRI's noon-time hmF2 values at six different geographic stations. It was found that the correlation between hmF2 and the macro-indices more accurately represented the variation of this correlation with latitude found in previous studies than if we used an isolated conventional index, such as SSN and AE. We also found that the daily maximum value of the Polar Cap Index was dependent on both solar and geomagnetic activity, but the closely-related cross-Polar Cap Potential was solely associated with elevated levels of geomagnetic activity, which is a unique result compared to previous studies. We argue that the discrepancy can be explained by the difference in experiment designs between the two studies. This paper demonstrates the usefulness of EDA in space weather studies of large multivariate data sets. Published by Elsevier Ltd. on behalf of COSPAR.

关键词： Ionosphere Space weather hmF2 exploratory data analysis Principle Components analysis Common Factor analysis

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Butanol production from lignocellulosic biomass: revisiting fermentation performance indicators with exploratory data analysis

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BIOTECHNOLOGY FOR BIOFUELS 2019年第1期12卷 1-15页

作者： Birgen, Cansu Duerre, Peter Preisig, Heinz A. Wentzel, Alexander NTNU Dept Chem Engn N-7491 Trondheim Norway Ulm Univ Inst Microbiol & Biotechnol D-89069 Ulm Germany SINTEF Ind N-7465 Trondheim Norway

After just more than 100years of history of industrial acetone-butanol-ethanol (ABE) fermentation, patented by Weizmann in the UK in 1915, butanol is again today considered a promising biofuel alternative based on several advantages compared to the more established biofuels ethanol and methanol. Large-scale fermentative production of butanol, however, still suffers from high substrate cost and low product titers and selectivity. There have been great advances the last decades to tackle these problems. However, understanding the fermentation process variables and their interconnectedness with a holistic view of the current scientific state-of-the-art is lacking to a great extent. To illustrate the benefits of such a comprehensive approach, we have developed a dataset by collecting data from 175 fermentations of lignocellulosic biomass and mixed sugars to produce butanol that reported during the past three decades of scientific literature and performed an exploratory data analysis to map current trends and bottlenecks. This review presents the results of this exploratory data analysis as well as main features of fermentative butanol production from lignocellulosic biomass with a focus on performance indicators as a useful tool to guide further research and development in the field towards more profitable butanol manufacturing for biofuel applications in the future.

关键词： Butanol Clostridia ABE fermentation Lignocellulosic biomass Mixed sugars exploratory data analysis

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A Survey of Unsupervised Generative Models for exploratory data analysis and Representation Learning

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ACM COMPUTING SURVEYS 2021年第5期54卷 1–40页

作者： Abukmeil, Mohanad Ferrari, Stefano Genovese, Angelo Piuri, Vincenzo Scotti, Fabio Univ Milan Via Giovanni Celoria 18 I-20133 Milan Italy

For more than a century, the methods for data representation and the exploration of the intrinsic structures of data have developed remarkably and consist of supervised and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high and the data can come in messy, incomplete, unlabeled, or corrupted forms. Consequently, discovering the hidden structure buried inside such data becomes highly challenging. From this perspective, exploratory data analysis plays a substantial role in learning the hidden structures that encompass the significant features of the data in an ordered manner by extracting patterns and testing hypotheses to identify anomalies. Unsupervised generative learning models are a class of machine learning models characterized by their potential to reduce the dimensionality, discover the exploratory factors, and learn representations without any predefined labels;moreover, such models can generate the data from the reduced factors' domain. The beginner researchers can find in this survey the recent unsupervised generative learning models for the purpose of data exploration and learning representations;specifically, this article covers three families of methods based on their usage in the era of big data: blind source separation, manifold learning, and neural networks, from shallow to deep architectures.

关键词： Blind source separation manifold learning neural networks exploratory data analysis representation learning explainable machine learning unsupervised deep learning

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IMPROVED ATMOSPHERIC UNDERSTANDING THROUGH exploratory data-analysis AND COMPLEMENTARY MODELING - THE URBAN K-PB-C SYSTEM

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ATMOSPHERIC ENVIRONMENT 1994年第8期28卷 1359-1369页

作者： CURRIE, LA SHEFFIELD, AE RIEDERER, GE ALLEGHENY COLL MEADVILLE PA 16335 USA UNIV MARYLAND College Pk MD 20742 USA

Univariate and multivariate exploration of urban carbonaceous aerosol data have revealed some interesting and important facets of aerosol carbon characteristics, sources and modeling assumptions. data were obtained from wintertime sampling in Albuquerque-a time and location for which the two-source (motor vehicle, woodburning) aerosol carbon hypothesis is believed valid. Carbon isotopic tracers (C-14, C-12) provided a unique, absolute and direct means for tracing and quantifying fossil carbon and wood carbon contributions to the total aerosol carbon. Lead and mineral-corrected potassium served as unique, but indirect and uncalibrated (non-absolute) tracers for the two carbonaceous aerosol sources. Results for the two models showed excellent consistency for daytime carbon and good agreement for most samples, but the indirect tracer model underpredicted woodcarbon aerosol at night. Multivariate data exploration, including temperature as well as both elemental and organic (polycyclic) tracers suggested that the regression lack of fit, and excess nocturnal wood carbon were due to a third 'statistical'' component in the data. which in turn reflects a chemical non-linearity, apparently associated with volatility. Important conclusions from analysis of the combined isotopic, elemental and organic data from this study are that: (1) high molecular weight, condensed polycyclic hydrocarbons [benzo (ghi) perylene and coronene] appeared to be effective tracers for motor-vehicle carbon;and (2) at least two independent aerosol carbon components derived from woodburning, one of which-presumably involving volatile-to-aerosol carbon condensation-was uncoupled from elemental (K) tracer emissions from woodburning. Finally, some insight was gained into the process of characterizing urban and regional aerosol ''fingerprints'' on the basis of the observed variance-covariance structure of their isotopic and elemental composition. This was illustrated for the wintertime Albuquerque atmosphe

关键词： CARBONACEOUS AEROSOL URBAN POLLUTION SOURCE APPORTIONMENT CARBON ISOTOPES ELEMENTAL TRACERS MULTIPLE LINEAR REGRESSION POLYCYCLIC AROMATIC HYDROCARBONS exploratory data analysis URBAN ATMOSPHERIC SIGNATURE WOODBURNING AEROSOL MOTOR VEHICLE AEROSOL BIOMASS BURNING

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COMPUTER JOURNAL 2023年第1期66卷 35-46页

作者： Khanam, Sana Tanweer, Safdar Khalid, Syed Sibtain Jamia Hamdard Dept Comp Sci & Engn Mehrauli Badarpur RdNear Batra HospBlock D Delhi 110062 India

With the emergence of social media and the Internet, bulks of data are generated every day in different fields. However, filtering out useful information, which can be turned into knowledge, requires significant effort. This can be achieved using various Big data Technologies, statistics, data mining and a variety of machine learning algorithms that corporates and businesses are using to improve their performance every day for a long time now. In this paper, we analyze YouTube trending video data that analyses the target that grabs the user's attention over a relatively short time. We present our analysis by measuring, mining, analyzing and comparing key aspects of time-series YouTube data with respect to its view and audience response statistics from 40 000 trending YouTube videos collected over 205 days. We have performed an exploratory data analysis (EDA) on all of it's aspect to get data insights and used statistics to find similarities between them to understand viewing pattern of different video categories. We also compare and observe the variation of activity over time with the nature of the event that affects the quality of our analysis. We make the viewership pattern clear belonging to each category separately by direct analysis. The results and findings can help us predict the category of the video that could probably be trending next in the upcoming days and help individuals get boosted views and performance when they upload video as per the predictions and analysis.

关键词： analytics big data YouTube machine learning statistics data science exploratory data analysis

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A richly interactive exploratory data analysis and visualization tool using electronic medical records

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2015年第1期15卷 1-14页

作者： Huang, Chih-Wei Lu, Richard Iqbal, Usman Lin, Shen-Hsien Phung Anh (Alex) Nguyen Yang, Hsuan-Chia Wang, Chun-Fu Li, Jianping Ma, Kwan-Liu Li, Yu-Chuan (Jack) Jian, Wen-Shan Taipei Med Univ Grad Inst Biomed Informat Coll Med Sci & Technol Taipei Taiwan Taipei Med Univ Int Ctr Hlth Informat Technol ICHIT Taipei Taiwan Natl Yang Ming Univ Inst Biomed Informat Taipei 112 Taiwan Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Wan Fang Hosp Dept Dermatol Taipei Taiwan Taipei Med Univ Sch Hlth Care Adm Taipei Taiwan Macau Univ Sci & Technol Fac Hlth Sci Macau Peoples R China

Background: Electronic medical records (EMRs) contain vast amounts of data that is of great interest to physicians, clinical researchers, and medial policy makers. As the size, complexity, and accessibility of EMRs grow, the ability to extract meaningful information from them has become an increasingly important problem to solve. Methods: We develop a standardized data analysis process to support cohort study with a focus on a particular disease. We use an interactive divide-and-conquer approach to classify patients into relatively uniform within each group. It is a repetitive process enabling the user to divide the data into homogeneous subsets that can be visually examined, compared, and refined. The final visualization was driven by the transformed data, and user feedback direct to the corresponding operators which completed the repetitive process. The output results are shown in a Sankey diagram-style timeline, which is a particular kind of flow diagram for showing factors' states and transitions over time. Results: This paper presented a visually rich, interactive web-based application, which could enable researchers to study any cohorts over time by using EMR data. The resulting visualizations help uncover hidden information in the data, compare differences between patient groups, determine critical factors that influence a particular disease, and help direct further analyses. We introduced and demonstrated this tool by using EMRs of 14,567 Chronic Kidney Disease (CKD) patients. Conclusions: We developed a visual mining system to support exploratory data analysis of multi-dimensional categorical EMR data. By using CKD as a model of disease, it was assembled by automated correlational analysis and human-curated visual evaluation. The visualization methods such as Sankey diagram can reveal useful knowledge about the particular disease cohort and the trajectories of the disease over time.

关键词： Systemic Lupus Erythematosus Chronic Kidney Disease Time Window Chronic Kidney Disease Patient exploratory data analysis

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Airborne laser scanning: exploratory data analysis indicates potential variables for classification of individual trees or forest stands according to species

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2005年第5期59卷 289-309页

作者： Moffiet, T Mengersen, K Witte, C King, R Denham, R Univ Newcastle Sch Math Callaghan NSW 2308 Australia Univ Newcastle Sch Phys Sci Callaghan NSW 2308 Australia Queensland Univ Technol Sch Math Sci Brisbane Qld 4001 Australia Queensland Dept Nat Resources & Mines Indooroopilly Qld 4068 Australia

Understanding your data through exploratory data analysis is a necessary first stage of data analysis particularly for observational data. The checking of data integrity and understanding the distributions, correlations and relationships between potentially important variables is a fundamental part of the analysis process prior to model development and hypothesis testing. In this paper, exploratory data analysis is used to assess the potential of laser return type and return intensity as variables for classification of individual trees or forest stands according to species. For narrow footprint lidar instruments that record up to two return amplitudes for each output pulse, the usual preclassification of return data into first and last intensity returns camouflages the fact that a number of the return signals have only "single amplitude' (singular) returns. The importance of singular returns for species discrimination has received little discussion in the remote sensing literature. A map view of the different types of returns overlaid on field species data indicated that it is possible to visually distinguish between vegetation types that produce a high proportion of singular returns, compared to vegetation types that produce a lower proportion of singular returns, at least when using a specific laser footprint size. Using lidar data and the corresponding field data derived from a subtropical woodland area of South East Queensland, Australia, map scatterplots of return types combined with field data enabled, in some cases, visual discrimination at the individual tree level between White Cypress Pine (Callitris glaucophylla) and Poplar Box (Eucalyptus populnea). While a clear distinction between these two species was not always visually obvious at the individual tree level, due to other extraneous sources of variation in the dataset, the observation was supported in general at the site level. Sites dominated by Poplar Box generally exhibited a lower proportion of sin

关键词： lidar intensity return exploratory data analysis species discrimination data visualization

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