检索结果-南通市图书馆

Acta Geologica Sinica(English Edition) 2021年第4期95卷 1357-1365页

作者： JANG Gwang-Hyok WON Hyon-Chol HWANG Bo-Hyon CHOI Chol-Man Faculty of Geology Kim Il Sung UniversityPyongyangDemocratic People's Republic of Korea

A factor analysis was applied to soil geochemical data to define anomalies related to buried Pb-Zn mineralization.A favorable main factor with a strong association of the elements Zn,Cu and Pb,related to mineralization,was selected for interpretation.The median+2 MAD(median absolute deviation)method of exploratory data analysis(EDA)and C-A(concentration-area)fractal modeling were then applied to the Mahalanobis distance,as defined by Zn,Cu and Pb from the factor analysis to set the thresholds for defining multi-element anomalies.As a result,the median+2 MAD method more successfully identified the Pb-Zn mineralization than the C-A fractal model.The soil anomaly identified by the median+2 MAD method on the Mahalanobis distances defined by three principal elements(Zn,Cu and Pb)rather than thirteen elements(Co,Zn,Cu,V,Mo,Ni,Cr,Mn,Pb,Ba,Sr,Zr and Ti)was the more favorable reflection of the ore body.The identified soil geochemical anomalies were compared with the in situ economic Pb-Zn ore bodies for validation.The results showed that the median+2 MAD approach is capable of mapping both strong and weak geochemical anomalies related to buried Pb-Zn mineralization,which is therefore useful at the reconnaissance drilling stage.

关键词： factor analysis exploratory data analysis Mahalanobis distance multi-element Unpha

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

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

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

Improving symbolic data visualization for pattern recognition and knowledge discovery

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Visual Informatics 2020年第1期4卷 23-31页

作者： Kadri Umbleja Manabu Ichino Hiroyuki Yaguchi School of Science and Engineering Tokyo Denki UniversityHatoyamaSaitama 350-0394Japan

This paper examines the visualization of symbolic data and considers the challenges rising from its complex structure.Symbolic data is usually aggregated from large data sets and used to hide entry specific details and to transform huge amounts of data(like big data)into analyzable quantities.It is also used to offer an overview in places where general trends are more important than individual details.Symbolic data comes in many forms like intervals,histograms,categories and modal multi-valued objects.Symbolic data can also be considered as a distribution.Currently,the de facto visualization approach for symbolic data is zoomstars which has many limitations.The biggest limitation is that the default distributions(histograms)are not supported in 2D as additional dimension is required.This paper proposes several new improvements for zoomstars which would enable it to visualize histograms in 2D by using a quantile or an equivalent interval approach.In addition,several improvements for categorical and modal variables are proposed for a clearer indication of presented categories.Recommendations for different approaches to zoomstars are offered depending on the data type and the desired goal.Furthermore,an alternative approach that allows visualizing the whole data set in comprehensive table-like graph,called shape encoding,is proposed.These visualizations and their usefulness are verified with three symbolic data sets in exploratory data mining phase to identify trends,similar objects and important features,detecting outliers and discrepancies in the data.

关键词： data visualization Symbolic data Zoomstar Shape encoding exploratory data analysis

A comprehensive review of tools for exploratory analysis of tabular industrial datasets

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Visual Informatics 2018年第4期2卷 235-253页

作者： Aindrila Ghosh Mona Nashaat James Miller Shaikh Quader Chad Marston Department of Electrical and Computer Engineering University of Alberta116 Street NWEdmontonT6G 1H9Canada Machine Learning Research IBM CanadaTorontoCanada Information Technology and Analytics IBM U.S.BostonUnited States

exploratory data analysis plays a major role in obtaining insights from data.Over the last two decades,researchers have proposed several visual data exploration tools that can assist with each step of the analysis process.Nevertheless,in recent years,data analysis requirements have changed significantly.With constantly increasing size and types of data to be analyzed,scalability and analysis duration are now among the primary concerns of researchers.Moreover,in order to minimize the analysis cost,businesses are in need of data analysis tools that can be used with limited analytical knowledge.To address these challenges,traditional data exploration tools have evolved within the last few years.In this paper,with an in-depth analysis of an industrial tabular dataset,we identify a set of additional exploratory requirements for large datasets.Later,we present a comprehensive survey of the recent advancements in the emerging field of exploratory data analysis.We investigate 50 academic and non-academic visual data exploration tools with respect to their utility in the six fundamental steps of the exploratory data analysis process.We also examine the extent to which these modern data exploration tools fulfill the additional requirements for analyzing large datasets.Finally,we identify and present a set of research opportunities in the field of visual exploratory data analysis.

关键词： exploratory data analysis Industrial tabular data Interactive visualization Systematic literature review Research opportunities

Uncovering the Online Social Structure Surrounding COVID-19

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Social Computing 2021年第2期2卷 157-165页

作者： Philip D.Waggoner Robert Y.Shapiro Samuel Frederick Ming Gong Institute for Social and Economic Research and Policy Columbia UniversityNew York CityNY 10027USA Department of Political Science Columbia UniversityNew York CityNY 10027USA. Department of Quantitative Methods in the Social Sciences Columbia UniversityNew York CityNY 10027USA

How do people talk about COVID-19 online?To address this question,we offer an unsupervised framework that allows us to examine Twitter framings of the pandemic.Our approach employs a network-based exploration of social media data to identify,categorize,and understand communication patterns about the novel coronavirus on Twitter.The simplest structure that emerges from our analysis is the distinction between the internal/personal,external/global,and generic threat framings of the pandemic.This structure replicates in different Twitter samples and is validated using the variation of information measure,reflecting the significance and stability of our findings.Such an exploratory study is useful for understanding the contours of the natural,non-random structure in this online space.We contend that this understanding of structure is necessary to address a host of causal,supervised,and related questions downstream.

关键词： COVID-19 networks community detection twitter exploratory data analysis

Comparison of Principal Components analysis,Independent Components analysis and Common Components analysis

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of analysis and Testing 2018年第3期2卷 235-248页

作者： Douglas N.Rutledge INRA UMR GENIALAgroParisTechUniversitéParis-SaclayMassyFrance

The aim of this work is to describe and compare three exploratory chemometrical tools,principal components analysis,independent components analysis and common components analysis,the last one being a modification of the multi-block statistical method known as common components and specific weights analysis.The three methods were applied to a set of data to show the differences and similarities of the results obtained,highlighting their complementarity.

关键词： exploratory data analysis Chemometrics Principal components analysis Independent components analysis Common components analysis Common components and specific weights analysis

Churn Prediction Task in MOOC

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Computer Science Research 2019年第1期1卷 29-35页

作者： Lisitsyna Liubov Oreshin SA ITMO University Kronvrkskiy pr.49Saint Petersburg197101Russia

Churn prediction is a common task for machine learning applications in business.In this paper,this task is adapted for solving problem of low efficiency of massive open online courses(only 5%of all the students finish their course).The approach is presented on course“Methods and algorithms of the graph theory”held on national platform of online education in Russia.This paper includes all the steps to build an intelligent system to predict students who are active during the course,but not likely to finish it.The first part consists of constructing the right sample for prediction,EDA and choosing the most appropriate week of the course to make predictions on.The second part is about choosing the right metric and building models.Also,approach with using ensembles like stacking is proposed to increase the accuracy of predictions.As a result,a general approach to build a churn prediction model for online course is reviewed.This approach can be used for making the process of online education adaptive and intelligent for a separate student.

关键词： Machine learning data science exploratory data analysis Logistic regression Gradient boosting on trees Stacking Classification Ranking