Finding groups in data : an introduction to cluster analysis / Leonard Kaufman, Peter J. Rousseeuw.
Series Wiley series in probability and mathematical statisticsApplied probability and statisticsEditor: New York : Wiley, c1990Descripción: xiv, 342 p. : il. ; 24 cmISBN: 0471878766Tema(s): Cluster analysisOtra clasificación: 62H30 Recursos en línea: Publisher description1. Introduction [1] 1. Motivation, [1] 2. Types of Data and How to Handle Them, [3] 2.1 Interval-Scaled Variables, [4] 2.2 Dissimilarities, [16] 2.3 Similarities, [20] 2.4 Binary Variables, [22] 2.5 Nominal, Ordinal, and Ratio Variables, [28] 2.6 Mixed Variables, [32] 3. Which Clustering Algorithm to Choose, [37] 3.1 Partitioning Methods, [38] 3.2 Hierarchical Methods, [44] 4. A Schematic Overview of Our Programs, [50] 5. Computing Dissimilarities with the Program DAISY, [52] Exercises and Problems, [63] 2. Partitioning Around Medoids (Program PAM) [68] 1. Short Description of the Method, [68] 2. How to Use the Program PAM, [72] 2.1 Interactive Use and Input, [72] 2.2 Output, [80] 2.3 Missing Values, [88] 3. Examples, [92] *4. More on the Algorithm and the Program, [102] 4.1 Description of the Algorithm, [102] 4.2 Structure of the Program, [104] *5. Related Methods and References, [108] 5.1 The Zc-Medoid Method and Optimal Plant Location, [108] 5.2 Other Methods Based on the Selection of Representative Objects, [110] 5.3 Methods Based on the Construction of Central Points, [111] 5.4 Some Other Nonhierarchical Methods, [116] 5.5 Why Did We Choose the k-Medoid Method?, [117] 5.6 Graphical Displays, [119] Exercises and Problems, [123] 3. Clustering Large Applications (Program CLARA) [126] 1. Short Description of the Method, [126] 2. How to Use the Program CLARA, [127] 2.1 Interactive Use and Input, [127] 2.2 Output, [130] 2.3 Missing Values, [134] 3. An Example, [139] *4. More on the Algorithm and the Program, [144] 4.1 Description of the Algorithm, [144] 4.2 Structure of the Program, [146] 4.3 Limitations and Special Messages, [151] 4.4 Modifications and Extensions of CLARA, [153] *5. Related Methods and References, [155] 5.1 Partitioning Methods for Large Data Sets, [155] 5.2 Hierarchical Methods for Large Data Sets, [157] 5.3 Implementing CLARA on a Parallel Computer, [160] Exercises and Problems, [162] 4. Fuzzy Analysis (Program FANNY) [164] 1. The Purpose of Fuzzy Clustering, [164] 2. How to Use the Program FANNY, [166] 2.1 Interactive Use and Input, [167] 2.2 Output, [170] 3. Examples, [175] *4. More on the Algorithm and the Program, [182] 4.1 Description of the Algorithm, [182] 4.2 Structure of the Program, [188] *5. Related Methods and References, [189] 5.1 Fuzzy k-Means and the MND2 Method, [189] 5.2 Why Did We Choose FANNY?, [191] 5.3 Measuring the Amount of Fuzziness, [191] 5.4 A Graphical Display of Fuzzy Memberships, [195] Exercises and Problems, [197] 5. Agglomerative Nesting (Program AGNES) [199] 1. Short Description of the Method, [199] 2. How to Use the Program AGNES, [208] 2.1 Interactive Use and Input, [208] 2.2 Output, [209] 3. Examples, [214] *4. More on the Algorithm and the Program, [221] 4.1 Description of the Algorithm, [221] 4.2 Structure of the Program, [223] *5. Related Methods and References, [224] 5.1 Other Agglomerative Clustering Methods, [224] 5.2 Comparing Their Properties, [238] 5.3 Graphical Displays, [243] Exercises and Problems, [250] 6. Divisive Analysis (Program DIANA) [253] 1. Short Description of the Method, [253] 2. How to Use the Program DIANA, [259] 3. Examples, [263] *4. More on the Algorithm and the Program, [271] 4.1 Description of the Algorithm, [271] 4.2 Structure of the Program, [272] *5. Related Methods and References, [273] 5.1 Variants of the Selected Method, [273] 5.2 Other Divisive Techniques, [275] Exercises and Problems, [277] 7. Monothetic Analysis (Program MONA) [280] 1. Short Description of the Method, [280] 2. How to Use the Program MONA, [283] 2.1 Interactive Use and Input, [284] 2.2 Output, [287] 3. Examples, [290] *4. More on the Algorithm and the Program, [298] 4.1 Description of the Algorithm, [298] 4.2 Structure of the Program, [301] *5. Related Methods and References, [304] 5.1 Association Analysis, [304] 5.2 Other Monothetic Divisive Algorithms for Binary Data, [307] 5.3 Some Other Divisive Clustering Methods, [308] Exercises and Problems, [310] APPENDIX [312] 1. Implementation and Structure of the Programs, [312] 2. Running the Programs, [313] 3. Adapting the Programs to Your Needs, [316] 4. The Program CLUSPLOT, [318]
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"A Wiley-Interscience publication."
Incluye referencias bibliográficas (p. 320-331) e índices.
1. Introduction [1] --
1. Motivation, [1] --
2. Types of Data and How to Handle Them, [3] --
2.1 Interval-Scaled Variables, [4] --
2.2 Dissimilarities, [16] --
2.3 Similarities, [20] --
2.4 Binary Variables, [22] --
2.5 Nominal, Ordinal, and Ratio Variables, [28] --
2.6 Mixed Variables, [32] --
3. Which Clustering Algorithm to Choose, [37] --
3.1 Partitioning Methods, [38] --
3.2 Hierarchical Methods, [44] --
4. A Schematic Overview of Our Programs, [50] --
5. Computing Dissimilarities with the Program DAISY, [52] --
Exercises and Problems, [63] --
2. Partitioning Around Medoids (Program PAM) [68] --
1. Short Description of the Method, [68] --
2. How to Use the Program PAM, [72] --
2.1 Interactive Use and Input, [72] --
2.2 Output, [80] --
2.3 Missing Values, [88] --
3. Examples, [92] --
*4. More on the Algorithm and the Program, [102] --
4.1 Description of the Algorithm, [102] --
4.2 Structure of the Program, [104] --
*5. Related Methods and References, [108] --
5.1 The Zc-Medoid Method and Optimal Plant Location, [108] --
5.2 Other Methods Based on the Selection of Representative Objects, [110] --
5.3 Methods Based on the Construction of Central Points, [111] --
5.4 Some Other Nonhierarchical Methods, [116] --
5.5 Why Did We Choose the k-Medoid Method?, [117] --
5.6 Graphical Displays, [119] --
Exercises and Problems, [123] --
3. Clustering Large Applications (Program CLARA) [126] --
1. Short Description of the Method, [126] --
2. How to Use the Program CLARA, [127] --
2.1 Interactive Use and Input, [127] --
2.2 Output, [130] --
2.3 Missing Values, [134] --
3. An Example, [139] --
*4. More on the Algorithm and the Program, [144] --
4.1 Description of the Algorithm, [144] --
4.2 Structure of the Program, [146] --
4.3 Limitations and Special Messages, [151] --
4.4 Modifications and Extensions of CLARA, [153] --
*5. Related Methods and References, [155] --
5.1 Partitioning Methods for Large Data Sets, [155] --
5.2 Hierarchical Methods for Large Data Sets, [157] --
5.3 Implementing CLARA on a Parallel Computer, [160] --
Exercises and Problems, [162] --
4. Fuzzy Analysis (Program FANNY) [164] --
1. The Purpose of Fuzzy Clustering, [164] --
2. How to Use the Program FANNY, [166] --
2.1 Interactive Use and Input, [167] --
2.2 Output, [170] --
3. Examples, [175] --
*4. More on the Algorithm and the Program, [182] --
4.1 Description of the Algorithm, [182] --
4.2 Structure of the Program, [188] --
*5. Related Methods and References, [189] --
5.1 Fuzzy k-Means and the MND2 Method, [189] --
5.2 Why Did We Choose FANNY?, [191] --
5.3 Measuring the Amount of Fuzziness, [191] --
5.4 A Graphical Display of Fuzzy Memberships, [195] --
Exercises and Problems, [197] --
5. Agglomerative Nesting (Program AGNES) [199] --
1. Short Description of the Method, [199] --
2. How to Use the Program AGNES, [208] --
2.1 Interactive Use and Input, [208] --
2.2 Output, [209] --
3. Examples, [214] --
*4. More on the Algorithm and the Program, [221] --
4.1 Description of the Algorithm, [221] --
4.2 Structure of the Program, [223] --
*5. Related Methods and References, [224] --
5.1 Other Agglomerative Clustering Methods, [224] --
5.2 Comparing Their Properties, [238] --
5.3 Graphical Displays, [243] --
Exercises and Problems, [250] --
6. Divisive Analysis (Program DIANA) [253] --
1. Short Description of the Method, [253] --
2. How to Use the Program DIANA, [259] --
3. Examples, [263] --
*4. More on the Algorithm and the Program, [271] --
4.1 Description of the Algorithm, [271] --
4.2 Structure of the Program, [272] --
*5. Related Methods and References, [273] --
5.1 Variants of the Selected Method, [273] --
5.2 Other Divisive Techniques, [275] --
Exercises and Problems, [277] --
7. Monothetic Analysis (Program MONA) [280] --
1. Short Description of the Method, [280] --
2. How to Use the Program MONA, [283] --
2.1 Interactive Use and Input, [284] --
2.2 Output, [287] --
3. Examples, [290] --
*4. More on the Algorithm and the Program, [298] --
4.1 Description of the Algorithm, [298] --
4.2 Structure of the Program, [301] --
*5. Related Methods and References, [304] --
5.1 Association Analysis, [304] --
5.2 Other Monothetic Divisive Algorithms for Binary Data, [307] --
5.3 Some Other Divisive Clustering Methods, [308] --
Exercises and Problems, [310] --
APPENDIX [312] --
1. Implementation and Structure of the Programs, [312] --
2. Running the Programs, [313] --
3. Adapting the Programs to Your Needs, [316] --
4. The Program CLUSPLOT, [318] --
MR, 91e:62159
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