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Group-Wise Principal Component analysis for exploratory data analysis

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2017年第3期26卷 501-512页

作者： Camacho, Jose Rodriguez-Gomez, Rafael A. Saccenti, Edoardo Univ Granada Dept Signal Theory Networking & Commun Granada Spain Wageningen Univ & Res Ctr Lab Syst & Synthet Biol Wageningen Netherlands

In this article, we propose a new framework for matrix factorization based on principal component analysis (PCA) where sparsity is imposed. The structure to impose sparsity is defined in terms of groups of correlated variables found in correlation matrices or maps. The framework is based on three new contributions: an algorithm to identify the groups of variables in correlation maps, a visualization for the resulting groups, and a matrix factorization. Together with a method to compute correlation maps with minimum noise level, referred to as missing-data for exploratory data analysis (MEDA), these three contributions constitute a complete matrix factorization framework. Two real examples are used to illustrate the approach and compare it with PCA, sparse PCA, and structured sparse PCA. Supplementary materials for this article are available online.

关键词： exploratory data analysis Matrix factorization Missing-data Sparsity Visualization

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An exploratory data analysis of the temperature fluctuations in a spreading fire

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ENVIRONMETRICS 2014年第6期25卷 443-453页

作者： Brillinger, David R. Finney, Mark A. Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA US Forest Serv USDA Fire Sci Lab Rocky Mt Res Stn Missoula MT 59807 USA

A series of fire experiments were carried out in a wind tunnel at the United States Forest Service's Fire Science Laboratory in Missoula, Montana. The experiments involved tines cut out of pieces of cardboard. The pieces were laid out in comb-like strips parallel to each other along a testbed. They were ignited at the windward end of the testbed. The progress of the fire was monitored by thermocouples, recording temperature, set out equidistantly up the middle of the testbed. Goals of the experiment included improved understanding of wildfire spread and the development of practical tools for wild land fire managers to employ. There was to be a search for regular pulsing in the series and any other interesting phenomena. This paper presents the results of a variety of exploratory data analyses meant to elicit information concerning the series before commencing probability modeling. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

关键词： nonstationary exploratory data analysis fire progression signal-generated noise temperatures wind tunnel

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An exploratory data analysis (EDA) of the paths of moving animals

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2004年第1-2期122卷 43-63页

作者： Brillinger, DR Preisler, HK Ager, AA Kie, JG Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA USDA Forest Serv Pacific SW Res Stn Albany CA 94710 USA USDA Forest Serv Pacific NW Res Stn La Grande OR 97850 USA

This work presents an exploratory data analysis of the trajectories of deer and elk moving about in the Starkey Experimental Forest and Range in eastern Oregon. The animals' movements may be affected by habitat variables and the behavior of the other animals. In the work of this paper a stochastic differential equation-based model is developed in successive stages. Equations of motion are set down motivated by corresponding equations of physics. Functional parameters appearing in the equations are estimated nonparametrically and plots of vector fields of animal movements are prepared. Residuals are used to look for interactions amongst the movements of the animals. There are exploratory analyses of various sorts. Statistical inferences are based on Fourier transforms of the data, which are unequally spaced. The sections of the paper start with motivating quotes and aphorisms from the writings of John W. Tukey. (C) 2003 Elsevier B.V. All rights reserved.

关键词： dependence deer elk exploratory data analysis fourier analysis paths starkey experimental forest time series John W. Tukey vector field plots

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exploratory data analysis and Artificial Neural Network for Prediction of Leptospirosis Occurrence in Seremban, Malaysia Based on Meteorological data

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FRONTIERS IN EARTH SCIENCE 2020年 8卷

作者： Rahmat, Fariq Zulkafli, Zed Ishak, Asnor Juraiza Noor, Samsul Bahari Mohd Yahaya, Hazlina Masrani, Afiqah Univ Putra Malaysia Dept Elect & Elect Engn Serdang Malaysia Univ Puta Malaysia Dept Civil Engn Serdang Malaysia Negeri Sembilan State Dept Hlth Seremban Malaysia

Leptospirosis outbreaks in various parts of the world have been linked to changes in the weather. Furthermore, the effects have been shown to occur at different lags of up to 10 months, affecting the performance of simulation models that predict leptospirosis occurrence. In Malaysia, the link between different weather parameters, at different time lags, has yet to be established despite an increasing number of cases in recent years. In this study, a combination of data mining and machine learning is used to analyze, capture, and predict the relation between leptospirosis occurrence and temperature, rainfall, and relative humidity using the Seremban district in Malaysia as a case study. First, the optimal time lags for rainfall were determined using graphical exploratory data analysis (EDA) while non-graphical EDA was used for temperature. Then, an artificial neural network (ANN) model is developed to classify the combination of selected features into disease occurrence and non-occurrence using back-propagation training, optimizing the number of hidden layers and hidden nodes. The success is measured using accuracy, sensitivity, and specificity of each model. EDA has shown that leptospirosis occurrence in Seremban is highly correlated with weekly average temperature at lag 16 weeks and weekly rainfall amount at lag 12-20 weeks. Using these selected features, the ANN model achieved the highest accuracy, sensitivity, and specificity at 84.00, 86.44, and 79.33%, respectively. Overall, the EDA approach has increased the accuracy of the predictive model by 13.30-31.26% from the baseline models.

关键词： artificial neural network exploratory data analysis predictive modeling leptospirosis meteorological data

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Using Spearman's correlation coefficients for exploratory data analysis on big dataset

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2016年第14期28卷 3866-3878页

作者： Xiao, Chengwei Ye, Jiaqi Esteves, Rui Maximo Rong, Chunming Univ Stavanger Dept Elect Engn & Comp Sci Stavanger Norway Natl Oilwell Varco Dept Condit Monitoring Stavanger Norway

Correlation analysis is both popular and useful in a number of social networking research, particularly in the exploratory data analysis. In this paper, three well-known and often-used correlation coefficients, Pearson product-moment correlation coefficient, Spearman, and Kendall rank correlation coefficients, are compared from definition to application domain. Based on the characteristics of the pump's vibration dataset, the non-parametric and distribution-free Spearman rank correlation coefficient is introduced to analyze the relationship between the pump's working state and each of the 207'880 variables. The percentage of variables and exact variables' tables with high Spearman's correlation coefficients for states I and II, states I and III, states II and III, and three states in different files are obtained respectively, which has important valuation for the future research of the unsupervised machine learning system. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： exploratory data analysis correlation analysis Spearman correlation coefficient p-value vibration analysis pump state

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Tor Marketplaces exploratory data analysis: The Drugs Case 11th

Tor Marketplaces Exploratory Data Analysis: The Drugs Case

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11th International Conference on Global Security, Safety, and Sustainability (ICGS3)

作者： Celestini, Alessandro Me, Gianluigi Mignone, Mara IAC CNR Inst Appl Comp Via Taurini 19 Rome Italy Luiss Guido Carli Univ CERSI Viale Romania 37 Rome Italy RISSC Res Ctr Secur & Crime Via Casoni 2 I-36040 Torri Di Quartesolo VI Italy

ISBN: (纸本)9783319510644;9783319510637

The anonymous marketplaces ecosystem represents a new channel for black market/goods and services, offering a huge variety of illegal items. For many darknet marketplaces, the overall sales incidence is not (yet) comparable with the correspondent physical market;however, since it represents a further trade channel, providing opportunities to new and old forms of illegal trade with worldwide customers, anonymous trading should be carefully studied, via regular crawling and data analysis, in order to detect new trends in illegal goods and services (physical and digital), new drug substances and sources and alternative paths to import socially dangerous goods (e.g. drugs, weapons). Such markets, based on e-commerce retail leaders model, e.g. Amazon and E-bay, are designed with ease of use in mind, using off-the-shelf web technologies where users have their own profiles and credentials, acting as sellers, posting offers, or buyers, posting reviews or both. This lead to very poor data quality related to market offers and related, possible feedback, increasing the complexity of extraction of reliable data. In this paper we present an approaching methodology to crawl and manipulate data for analysis of illicit drugs trade taking place in such marketplaces. We focus our analysis on AlphaBay, Nucleus and East India Company and we will show how to prepare data for the analysis and how to carry on the preliminary data investigation, based on the exploratory data analysis.

关键词： Tor Marketplaces Dark web exploratory data analysis

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exploratory data analysis Based Short-Term Electrical Load Forecasting: A Comprehensive analysis

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ENERGIES 2021年第17期14卷 5510页

作者： Javed, Umar Ijaz, Khalid Jawad, Muhammad Ansari, Ejaz A. Shabbir, Noman Kutt, Lauri Husev, Oleksandr COMSATS Univ Islamabad Dept Elect & Comp Engn Lahore 54000 Pakistan Univ Management & Technol Elect Engn Dept Lahore 54000 Pakistan Tallinn Univ Technol Dept Elect Power Engn & Mechatron EE-12616 Tallinn Estonia

Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN-LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN-PSO, KNN, ANN-LM with two hidden layers and bootstrap aggregation models.

关键词： short-term load forecasting time-series forecasting exploratory data analysis neural network Levenberg-Marquardt

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The artificial intelligence advantage: Supercharging exploratory data analysis

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KNEE SURGERY SPORTS TRAUMATOLOGY ARTHROSCOPY 2024年第11期32卷 3039-3042页

作者： Oettl, Felix C. Oeding, Jacob F. Feldt, Robert Ley, Christophe Hirschmann, Michael T. Samuelsson, Kristian Hosp Special Surg New York NY USA Univ Zurich Balgrist Univ Hosp Dept Orthoped Surg Zurich Switzerland Univ Gothenburg Sahlgrenska Acad Inst Clin Sci Dept Orthopaed Gothenburg Sweden Sahlgrenska Sports Med Ctr Gothenburg Sweden Mayo Clin Alix Sch Med Rochester MN USA Chalmers Univ Technol Dept Comp Sci & Engn Gothenburg Sweden Univ Luxembourg Dept Math Esch Sur Alzette Luxembourg Kantonsspital Baselland Dept Orthoped Surg & Traumatol Liestal Switzerland Sahlgrens Univ Hosp Dept Orthopaed Molndal Sweden

Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions. Tree-based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real-world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data-driven decision-making across various scientific *** of Evidence: Level V - Expert opinion.

关键词： artificial intelligence exploratory data analysis feature engineering machine learning orthopedic research

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Predicting the Subcellular Localization of Human Proteins Using Machine Learning and exploratory data analysis

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Genomics, Proteomics & Bioinformatics 2006年第2期4卷 120-133页

作者： George K. Acquaah-Mensah Sonia M. Leach Chittibabu Guda Department of Pharmaceutical Sciences School of Pharmacy-Worcester Massachusetts College of Pharmacyand Health Sciences Worcester MA 01608-1715 USA Center for Computational Pharmacology Departmentof Pharmacology University of Colorado School of Medicine Aurora CO 80010 USA Gen*NY*Sis Centerfor Excellence in Cancer Genomics Department of Epidemiology and Biostatistics State University of NewYork at Albany Rensselaer NY 12144-3456 USA.

Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. In this study, we use Machine Learning and exploratory data analysis （EDA） techniques to examine and characterize amino acid sequences of human proteins localized in nine cellular compartments. A dataset of 3,749 protein sequences representing human proteins was extracted from the SWISS-PROT database. Feature vectors were created to capture specific amino acid sequence characteristics. Relative to a Support Vector Machine, a Multi-layer Perceptron, and a Naive Bayes classifier, the C4.5 Decision Tree algorithm was the most consistent performer across all nine compartments in reliably predicting the subcellular localization of proteins based on their amino acid sequences （average Precision=0.88; average Sensitivity=0.86）. Furthermore, EDA graphics characterized essential features of proteins in each compartment. As examples, proteins localized to the plasma membrane had higher proportions of hydrophobic amino acids; cytoplasmic proteins had higher proportions of neutral amino acids; and mitochondrial proteins had higher proportions of neutral amino acids and lower proportions of polar amino acids. These data showed that the C4.5 classifier and EDA tools can be effective for characterizing and predicting the subcellular localization of human proteins based on their amino acid sequences.

关键词： subcellular localization Machine Learning exploratory data analysis Decision Tree

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Chicago Traffic Collision data analysis Based on Multi-Component analysis and exploratory data analysis 20

Chicago Traffic Collision Data Analysis Based on Multi-Compo...

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20th COTA International Conference of Transportation Professionals (CICTP) - Advanced Transportation, Enhanced Connection

作者： Zhang, Wenzhao Zhang, Shen Harbin Inst Technol Sch Transportat Sci & Engn Harbin Peoples R China

ISBN: (数字)9780784483053

ISBN: (纸本)9780784483053

Efficient traffic management systems are especially necessary for large cities. Chicago is the third largest city in the United States. The city's recent collision data is not optimistic, and citizens' lives are at risk. Current research on the topic has some shortcomings, such as, only a few traffic vehicle collision models exist, the difficulty in applying related methods to vehicle collision data, and the complexity of the relationships between the myriad of variables. This paper analyzes Chicago traffic crash data from 2015 to 2019. Geographical location, multi-component, and exploratory data analyses are used to analyze the relationships of variables, i.e., collision date, road surface and weather conditions, and vehicle condition. The results showed that good driving conditions and fine weather rarely cause vehicle collisions;while, high alcohol content and improper vehicle speed has the opposite effect. This study offers meaningful contribution to the advancement of traffic vehicle collision data models.

关键词： Traffic vehicle collision data exploratory data analysis Multivariate analysis

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