Interactive Graphics for Data Analysis: Principles and Examples discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets.Fundamentals of Interactive Statistical Graphics The first part of the book summarizes principles and methodology, demonstrating how the different graphical representations of variables of a dataset are effectively used in an interactive setting. The authors introduce the most important plots and their interactive controls. They also examine various types of data, relations between variables, and plot ensembles.Case Studies Illustrate the Principles The second section focuses on nine case studies. Each case study describes the background, lists the main goals of the analysis and the variables in the dataset, shows what further numerical procedures can add to the graphical analysis, and summarizes important findings. Wherever applicable, the authors also provide the numerical analysis for datasets found in Cox and Snells landmark book.This full-color text shows that interactive graphical methods complement the traditional statistical toolbox to achieve more complete, easier to understand, and easier to interpret analyses.
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INTRODUCTION: HISTORICAL REMARKS WHAT IS CLUSTERING Exemplary Problems Bird's Eye View WHAT IS DATA Feature Characteristics Bivariate Analysis Feature Space and Data Scatter Preprocessing and Standardizing Mixed Data K-MEANS CLUSTERING Conventional K-Means Initialization of K-Means Intelligent K-Means Interpretation Aids Overall Assessment WARD HIERARCHICAL CLUSTERING Agglomeration: Ward Algorithm Divisive Clustering with Ward Criterion Conceptual Clustering Extensions of Ward Clustering Overall Assessment DATA RECOVERY MODELS Statistics Modeling as Data Recovery Data Recovery Model for K-Means Data Recovery Models for Ward Criterion Extensions to Other Data Types One-by-One Clustering Overall Assessment DIFFERENT CLUSTERING APPROACHES Extensions of K-Means Clustering Graph-Theoretic Approaches Conceptual Description of Clusters Overall Assessment GENERAL ISSUES Feature Selection and Extraction Data Pre-Processing and Standardization Similarity on Subsets and Partitions Dealing with Missing Data Validity and Reliability Overall Assessment CONCLUSION: Data Recovery Approach in Clustering BIBLIOGRAPHY Each chapter also contains a section of Base Words
{"title":"Clustering for data mining - a data recovery approach","authors":"B. Mirkin","doi":"10.1201/9781420034912","DOIUrl":"https://doi.org/10.1201/9781420034912","url":null,"abstract":"INTRODUCTION: HISTORICAL REMARKS WHAT IS CLUSTERING Exemplary Problems Bird's Eye View WHAT IS DATA Feature Characteristics Bivariate Analysis Feature Space and Data Scatter Preprocessing and Standardizing Mixed Data K-MEANS CLUSTERING Conventional K-Means Initialization of K-Means Intelligent K-Means Interpretation Aids Overall Assessment WARD HIERARCHICAL CLUSTERING Agglomeration: Ward Algorithm Divisive Clustering with Ward Criterion Conceptual Clustering Extensions of Ward Clustering Overall Assessment DATA RECOVERY MODELS Statistics Modeling as Data Recovery Data Recovery Model for K-Means Data Recovery Models for Ward Criterion Extensions to Other Data Types One-by-One Clustering Overall Assessment DIFFERENT CLUSTERING APPROACHES Extensions of K-Means Clustering Graph-Theoretic Approaches Conceptual Description of Clusters Overall Assessment GENERAL ISSUES Feature Selection and Extraction Data Pre-Processing and Standardization Similarity on Subsets and Partitions Dealing with Missing Data Validity and Reliability Overall Assessment CONCLUSION: Data Recovery Approach in Clustering BIBLIOGRAPHY Each chapter also contains a section of Base Words","PeriodicalId":311591,"journal":{"name":"Computer science and data analysis series","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114245201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bayesian Reasoning. Introduction to Bayesian Networks. Inference in Bayesian Networks. Bayesian Network Applications. Bayesian Planning and Decision-Making. Bayesian Network Applications II. Learning Bayesian Networks I. Learning Bayesian Networks II. Causality vs. Probability. Knowledge Engineering with Bayesian Networks I. Knowledge Engineering with Bayesian Networks II. Application Software.
{"title":"Bayesian Artificial Intelligence","authors":"K. Korb, A. Nicholson","doi":"10.5860/choice.41-5948","DOIUrl":"https://doi.org/10.5860/choice.41-5948","url":null,"abstract":"Bayesian Reasoning. Introduction to Bayesian Networks. Inference in Bayesian Networks. Bayesian Network Applications. Bayesian Planning and Decision-Making. Bayesian Network Applications II. Learning Bayesian Networks I. Learning Bayesian Networks II. Causality vs. Probability. Knowledge Engineering with Bayesian Networks I. Knowledge Engineering with Bayesian Networks II. Application Software.","PeriodicalId":311591,"journal":{"name":"Computer science and data analysis series","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131320094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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