{"product_id":"clustering-methodology-for-symbolic-data-hardback-9780470713938","title":"Clustering Methodology for Symbolic Data (Hardback) 9780470713938","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eClustering Methodology for Symbolic Data\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eLynne Billard (Author), Edwin Diday (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470713938, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 25 October 2019\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages\u003cbr\u003e23.1 x 15.8 x 2.3 cm, 0.59 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eCovers everything readers need to know about clustering methodology for symbolic data—including new methods and headings—while providing a focus on multi-valued list data, interval data and histogram data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book presents all of the latest developments in the field of clustering methodology for symbolic data—paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples. The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses.\u003c\/p\u003e \u003cp\u003eFilled with examples, tables, figures, and case studies, \u003ci\u003eClustering Methodology for Symbolic Data\u003c\/i\u003e begins by offering chapters on data management, distance measures, general clustering techniques, partitioning, divisive clustering, and agglomerative and pyramid clustering. \u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides new classification methodologies for histogram valued data reaching across many fields in data science\u003c\/li\u003e \u003cli\u003eDemonstrates how to manage a large complex dataset into manageable datasets ready for analysis\u003c\/li\u003e \u003cli\u003eFeatures very large contemporary datasets such as multi-valued list data, interval-valued data, and histogram-valued data\u003c\/li\u003e \u003cli\u003eConsiders classification models by dynamical clustering\u003c\/li\u003e \u003cli\u003eFeatures a supporting website hosting relevant data sets \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eClustering Methodology for Symbolic Data\u003c\/i\u003e will appeal to practitioners of symbolic data analysis, such as statisticians and economists within the public sectors. It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bioengineering.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003e1 Introduction \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Symbolic Data: Basics \u003c\/b\u003e\u003cb\u003e7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Individuals, Classes, Observations, and Descriptions 8\u003c\/p\u003e \u003cp\u003e2.2 Types of Symbolic Data 9\u003c\/p\u003e \u003cp\u003e2.2.1 Multi-valued or Lists of Categorical Data 9\u003c\/p\u003e \u003cp\u003e2.2.2 Modal Multi-valued Data 10\u003c\/p\u003e \u003cp\u003e2.2.3 Interval Data 12\u003c\/p\u003e \u003cp\u003e2.2.4 Histogram Data 13\u003c\/p\u003e \u003cp\u003e2.2.5 Other Types of Symbolic Data 14\u003c\/p\u003e \u003cp\u003e2.3 How do Symbolic Data Arise? 17\u003c\/p\u003e \u003cp\u003e2.4 Descriptive Statistics 24\u003c\/p\u003e \u003cp\u003e2.4.1 Sample Means 25\u003c\/p\u003e \u003cp\u003e2.4.2 Sample Variances 26\u003c\/p\u003e \u003cp\u003e2.4.3 Sample Covariance and Correlation 28\u003c\/p\u003e \u003cp\u003e2.4.4 Histograms 31\u003c\/p\u003e \u003cp\u003e2.5 Other Issues 38\u003c\/p\u003e \u003cp\u003eExercises 39\u003c\/p\u003e \u003cp\u003eAppendix 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Dissimilarity, Similarity, and Distance Measures \u003c\/b\u003e\u003cb\u003e47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Some General Basic Definitions 47\u003c\/p\u003e \u003cp\u003e3.2 Distance Measures: List or Multi-valued Data 55\u003c\/p\u003e \u003cp\u003e3.2.1 Join and Meet Operators for Multi-valued List Data 55\u003c\/p\u003e \u003cp\u003e3.2.2 A Simple Multi-valued Distance 56\u003c\/p\u003e \u003cp\u003e3.2.3 Gowda–Diday Dissimilarity 58\u003c\/p\u003e \u003cp\u003e3.2.4 Ichino–Yaguchi Distance 60\u003c\/p\u003e \u003cp\u003e3.3 Distance Measures: Interval Data 62\u003c\/p\u003e \u003cp\u003e3.3.1 Join and Meet Operators for Interval Data 62\u003c\/p\u003e \u003cp\u003e3.3.2 Hausdorff Distance 63\u003c\/p\u003e \u003cp\u003e3.3.3 Gowda–Diday Dissimilarity 68\u003c\/p\u003e \u003cp\u003e3.3.4 Ichino–Yaguchi Distance 73\u003c\/p\u003e \u003cp\u003e3.3.5 de Carvalho Extensisons of Ichino–Yaguchi Distances 76\u003c\/p\u003e \u003cp\u003e3.4 Other Measures 79\u003c\/p\u003e \u003cp\u003eExercises 79\u003c\/p\u003e \u003cp\u003eAppendix 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Dissimilarity, Similarity, and Distance Measures: Modal Data \u003c\/b\u003e\u003cb\u003e83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Dissimilarity\/Distance Measures: Modal Multi-valued List Data 83\u003c\/p\u003e \u003cp\u003e4.1.1 Union and Intersection Operators for Modal Multi-valued List Data 84\u003c\/p\u003e \u003cp\u003e4.1.2 A Simple Modal Multi-valued List Distance 85\u003c\/p\u003e \u003cp\u003e4.1.3 Extended Multi-valued List Gowda–Diday Dissimilarity 87\u003c\/p\u003e \u003cp\u003e4.1.4 Extended Multi-valued List Ichino–Yaguchi Dissimilarity 90\u003c\/p\u003e \u003cp\u003e4.2 Dissimilarity\/Distance Measures: Histogram Data 93\u003c\/p\u003e \u003cp\u003e4.2.1 Transformation of Histograms 94\u003c\/p\u003e \u003cp\u003e4.2.2 Union and Intersection Operators for Histograms 98\u003c\/p\u003e \u003cp\u003e4.2.3 Descriptive Statistics for Unions and Intersections 101\u003c\/p\u003e \u003cp\u003e4.2.4 Extended Gowda–Diday Dissimilarity 104\u003c\/p\u003e \u003cp\u003e4.2.5 Extended Ichino–Yaguchi Distance 108\u003c\/p\u003e \u003cp\u003e4.2.6 Extended de Carvalho Distances 112\u003c\/p\u003e \u003cp\u003e4.2.7 Cumulative Density Function Dissimilarities 115\u003c\/p\u003e \u003cp\u003e4.2.8 Mallows’ Distance 117\u003c\/p\u003e \u003cp\u003eExercises 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 General Clustering Techniques \u003c\/b\u003e\u003cb\u003e119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Brief Overview of Clustering 119\u003c\/p\u003e \u003cp\u003e5.2 Partitioning 120\u003c\/p\u003e \u003cp\u003e5.3 Hierarchies 125\u003c\/p\u003e \u003cp\u003e5.4 Illustration 131\u003c\/p\u003e \u003cp\u003e5.5 Other Issues 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Partitioning Techniques \u003c\/b\u003e\u003cb\u003e149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Basic Partitioning Concepts 150\u003c\/p\u003e \u003cp\u003e6.2 Multi-valued List Observations 153\u003c\/p\u003e \u003cp\u003e6.3 Interval-valued Data 159\u003c\/p\u003e \u003cp\u003e6.4 Histogram Observations 169\u003c\/p\u003e \u003cp\u003e6.5 Mixed-valued Observations 177\u003c\/p\u003e \u003cp\u003e6.6 Mixture Distribution Methods 179\u003c\/p\u003e \u003cp\u003e6.7 Cluster Representation 186\u003c\/p\u003e \u003cp\u003e6.8 Other Issues 189\u003c\/p\u003e \u003cp\u003eExercises 191\u003c\/p\u003e \u003cp\u003eAppendix 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Divisive Hierarchical Clustering \u003c\/b\u003e\u003cb\u003e197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Some Basics 197\u003c\/p\u003e \u003cp\u003e7.1.1 Partitioning Criteria 197\u003c\/p\u003e \u003cp\u003e7.1.2 Association Measures 200\u003c\/p\u003e \u003cp\u003e7.2 Monothetic Methods 203\u003c\/p\u003e \u003cp\u003e7.2.1 Modal Multi-valued Observations 205\u003c\/p\u003e \u003cp\u003e7.2.2 Non-modal Multi-valued Observations 214\u003c\/p\u003e \u003cp\u003e7.2.3 Interval-valued Observations 216\u003c\/p\u003e \u003cp\u003e7.2.4 Histogram-valued Observations 225\u003c\/p\u003e \u003cp\u003e7.3 Polythethic Methods 236\u003c\/p\u003e \u003cp\u003e7.4 Stopping Rule \u003ci\u003eR \u003c\/i\u003e250\u003c\/p\u003e \u003cp\u003e7.5 Other Issues 257\u003c\/p\u003e \u003cp\u003eExercises 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Agglomerative Hierarchical Clustering \u003c\/b\u003e\u003cb\u003e261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Agglomerative Hierarchical Clustering 261\u003c\/p\u003e \u003cp\u003e8.1.1 Some Basic Definitions 261\u003c\/p\u003e \u003cp\u003e8.1.2 Multi-valued List Observations 266\u003c\/p\u003e \u003cp\u003e8.1.3 Interval-valued Observations 269\u003c\/p\u003e \u003cp\u003e8.1.4 Histogram-valued Observations 278\u003c\/p\u003e \u003cp\u003e8.1.5 Mixed-valued Observations 281\u003c\/p\u003e \u003cp\u003e8.1.6 Interval Observations with Rules 282\u003c\/p\u003e \u003cp\u003e8.2 Pyramidal Clustering 289\u003c\/p\u003e \u003cp\u003e8.2.1 Generality Degree 289\u003c\/p\u003e \u003cp\u003e8.2.2 Pyramid Construction Based on Generality Degree 297\u003c\/p\u003e \u003cp\u003e8.2.3 Pyramids from Dissimilarity Matrix 309\u003c\/p\u003e \u003cp\u003e8.2.4 Other Issues 312\u003c\/p\u003e \u003cp\u003eExercises 313\u003c\/p\u003e \u003cp\u003eAppendix 315\u003c\/p\u003e \u003cp\u003eReferences 317\u003c\/p\u003e \u003cp\u003eIndex 331\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics 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