Data clustering in C++ : an object-oriented approach /: an object-oriented approach. (©2011)

• Record Type: Book
• Other Titles: Data clustering in C plus plus
• Further Information: Note: Guojun Gan.
• Other Names: Gan, Guojun, 1979-
• Contents: Machine generated contents note: I. Data Clustering and C++ Preliminaries -- 1. Introduction to Data Clustering -- 1.1. Data Clustering -- 1.1.1. Clustering versus Classification -- 1.1.2. Definition of Clusters -- 1.2. Data Types -- 1.3. Dissimilarity and Similarity Measures -- 1.3.1. Measures for Continuous Data -- 1.3.2. Measures for Discrete Data -- 1.3.3. Measures for Mixed-Type Data -- 1.4. Hierarchical Clustering Algorithms -- 1.4.1. Agglomerative Hierarchical Algorithms -- 1.4.2. Divisive Hierarchical Algorithms -- 1.4.3. Other Hierarchical Algorithms -- 1.4.4. Dendrograms -- 1.5. Partitional Clustering Algorithms -- 1.5.1. Center-Based Clustering Algorithms -- 1.5.2. Search-Based Clustering Algorithms -- 1.5.3. Graph-Based Clustering Algorithms -- 1.5.4. Grid-Based Clustering Algorithms -- 1.5.5. Density-Based Clustering Algorithms -- 1.5.6. Model-Based Clustering Algorithms -- 1.5.7. Subspace Clustering Algorithms -- 1.5.8. Neural Network-Based Clustering Algorithms -- 1.5.9. Fuzzy Clustering Algorithms -- 1.6. Cluster Validity -- 1.7. Clustering Applications -- 1.8. Literature of Clustering Algorithms -- 1.8.1. Books on Data Clustering -- 1.8.2. Surveys on Data Clustering -- 1.9. Summary -- 2. The Unified Modeling Language -- 2.1. Package Diagrams -- 2.2. Class Diagrams -- 2.3. Use Case Diagrams -- 2.4. Activity Diagrams -- 2.5. Notes -- 2.6. Summary -- 3. Object-Oriented Programming and C++ -- 3.1. Object-Oriented Programming -- 3.2. The C++ Programming Language -- 3.3. Encapsulation -- 3.4. Inheritance -- 3.5. Polymorphism -- 3.5.1. Dynamic Polymorphism -- 3.5.2. Static Polymorphism -- 3.6. Exception Handling -- 3.7. Summary -- 4. Design Patterns -- 4.1. Singleton -- 4.2.Composite -- 4.3. Prototype -- 4.4. Strategy -- 4.5. Template Method -- 4.6. Visitor -- 4.7. Summary -- 5.C++ Libraries and Tools -- 5.1. The Standard Template Library -- 5.1.1. Containers -- 5.1.2. Iterators -- 5.1.3. Algorithms -- 5.2. Boost C++ Libraries -- 5.2.1. Smart Pointers -- 5.2.2. Variant -- 5.2.3. Variant versus Any -- 5.2.4. Tokenizer -- 5.2.5. Unit Test Framework -- 5.3. GNU Build System -- 5.3.1. Autoconf -- 5.3.2. Automake -- 5.3.3. Libtool -- 5.3.4. Using GNU Autotools -- 5.4. Cygwin -- 5.5. Summary -- II. A C++ Data Clustering Framework -- 6. The Clustering Library -- 6.1. Directory Structure and Filenames -- 6.2. Specification Files -- 6.2.1.configure.ac -- 6.2.2. Makefile.am -- 6.3. Macros and typedef Declarations -- 6.4. Error Handling -- 6.5. Unit Testing -- 6.6.Compilation and Installation -- 6.7. Summary -- 7. Datasets -- 7.1. Attributes -- 7.1.1. The Attribute Value Class -- 7.1.2. The Base Attribute Information Class -- 7.1.3. The Continuous Attribute Information Class -- 7.1.4. The Discrete Attribute Information Class -- 7.2. Records -- 7.2.1. The Record Class -- 7.2.2. The Schema Class -- 7.3. Datasets -- 7.4.A Dataset Example -- 7.5. Summary -- 8. Clusters -- 8.1. Clusters -- 8.2. Partitional Clustering -- 8.3. Hierarchical Clustering -- 8.4. Summary -- 9. Dissimilarity Measures -- 9.1. The Distance Base Class -- 9.2. Minkowski Distance -- 9.3. Euclidean Distance -- 9.4. Simple Matching Distance -- 9.5. Mixed Distance -- 9.6. Mahalanobis Distance -- 9.7. Summary -- 10. Clustering Algorithms -- 10.1. Arguments -- 10.2. Results -- 10.3. Algorithms -- 10.4.A Dummy Clustering Algorithm -- 10.5. Summary -- 11. Utility Classes -- 11.1. The Container Class -- 11.2. The Double-Key Map Class -- 11.3. The Dataset Adapters -- 11.3.1.A CSV Dataset Reader -- 11.3.2.A Dataset Generator -- 11.3.3.A Dataset Normalizer -- 11.4. The Node Visitors -- 11.4.1. The Join Value Visitor -- 11.4.2. The Partition Creation Visitor -- 11.5. The Dendrogram Class -- 11.6. The Dendrogram Visitor -- 11.7. Summary -- III. Data Clustering Algorithms -- 12. Agglomerative Hierarchical Algorithms -- 12.1. Description of the Algorithm -- 12.2. Implementation -- 12.2.1. The Single Linkage Algorithm -- 12.2.2. The Complete Linkage Algorithm -- 12.2.3. The Group Average Algorithm -- 12.2.4. The Weighted Group Average Algorithm -- 12.2.5. The Centroid Algorithm -- 12.2.6. The Median Algorithm -- 12.2.7. Ward's Algorithm -- 12.3. Examples -- 12.3.1. The Single Linkage Algorithm -- 12.3.2. The Complete Linkage Algorithm -- 12.3.3. The Group Average Algorithm -- 12.3.4. The Weighted Group Average Algorithm -- 12.3.5. The Centroid Algorithm -- 12.3.6. The Median Algorithm -- 12.3.7. Ward's Algorithm -- 12.4. Summary -- 13. DIANA -- 13.1. Description of the Algorithm -- 13.2. Implementation -- 13.3. Examples -- 13.4. Summary -- 14. The k-means Algorithm -- 14.1. Description of the Algorithm -- 14.2. Implementation -- 14.3. Examples -- 14.4. Summary -- 15. The c-means Algorithm -- 15.1. Description of the Algorithm -- 15.2. Implementaion -- 15.3. Examples -- 15.4. Summary -- 16. The k-prototypes Algorithm -- 16.1. Description of the Algorithm -- 16.2. Implementation -- 16.3. Examples -- 16.4. Summary -- 17. The Genetic k-modes Algorithm -- 17.1. Description of the Algorithm -- 17.2. Implementation -- 17.3. Examples -- 17.4. Summary -- 18. The FSC Algorithm -- 18.1. Description of the Algorithm -- 18.2. Implementation -- 18.3. Examples -- 18.4. Summary -- 19. The Gaussian Mixture Algorithm -- 19.1. Description of the Algorithm -- 19.2. Implementation -- 19.3. Examples -- 19.4. Summary -- 20.A Parallel k-means Algorithm -- 20.1. Message Passing Interface -- 20.2. Description of the Algorithm -- 20.3. Implementation -- 20.4. Examples -- 20.5. Summary -- A. Exercises and Projects -- B. Listings -- B.1. Files in Folder ClusLib -- B.1.1. Configuration File configure.ac -- B.1.2.m4 Macro File ac include. m4 -- B.1.3. Makefile -- B.2. Files in Folder cl -- B.2.1. Makefile -- B.2.2. Macros and typedef Declarations -- B.2.3. Class Error -- B.3. Files in Folder cl/algorithms -- B.3.1. Makefile -- B.3.2. Class Algorithm -- B.3.3. Class Average -- B.3.4. Class Centroid -- B.3.5. Class Cmean -- B.3.6. Class Complete -- B.3.7. Class Diana -- B.3.8. Class FSC -- B.3.9. Class GKmode -- B.3.10. Class GMC -- B.3.11. Class Kmean -- B.3.12. Class Kprototype -- B.3.13. Class LW -- B.3.14. Class Median -- B.3.15. Class Single -- B.3.16. Class Ward -- B.3.17. Class Weighted -- B.4. Files in Folder cl/clusters -- B.4.1. Makefile -- B.4.2. Class CenterCluster -- B.4.3. Class Cluster -- B.4.4. Class HClustering -- B.4.5. Class PClustering -- B.4.6. Class SubspaceCluster -- B.5. Files in Folder cl/datasets -- B.5.1. Makefile -- B.5.2. Class AttrValue -- B.5.3. Class AttrInfo -- B.5.4. Class CAttrInfo -- B.5.5. Class DAttrInfo -- B.5.6. Class Record -- B.5.7. Class Schema -- B.5.8. Class Dataset -- B.6. Files in Folder cl/distances -- B.6.1. Makefile -- B.6.2. Class Distance -- B.6.3. Class EuclideanDistance -- B.6.4. Class MahalanobisDistance -- B.6.5. Class MinkowskiDistance -- B.6.6. Class MixedDistance -- B.6.7. Class SimpleMatchingDistance -- B.7. Files in Folder cl/patterns -- B.7.1. Makefile -- B.7.2. Class DendrogramVisitor -- B.7.3. Class InternalNode -- B.7.4. Class LeafNode -- B.7.5. Class Node -- B.7.6. Class NodeVisitor -- B.7.7. Class JoinValueVisitor -- B.7.8. Class PCVisitor -- B.8. Files in Folder cl/utilities -- B.8.1. Makefile -- B.8.2. Class Container -- B.8.3. Class DataAdapter -- B.8.4. Class DatasetGenerator -- B.8.5. Class DatasetNormalizer -- B.8.6. Class DatasetReader -- B.8.7. Class Dendrogram -- B.8.8. Class nnMap -- B.8.9. Matrix Functions -- B.8.10. Null Types -- B.9. Files in Folder examples -- B.9.1. Makefile -- B.9.2. Agglomerative Hierarchical Algorithms -- B.9.3.A Divisive Hierarchical Algorithm -- B.9.4. The k-means Algorithm -- B.9.5. The c-means Algorithm -- B.9.6. The k-prototypes Algorithm -- B.9.7. The Genetic k-modes Algorithm -- B.9.8. The FSC Algorithm -- B.9.9. The Ganssian Mixture Clustering Algorithm -- B.9.10.A Parallel k-means Algorithm -- B.10. Files in Folder test-suite -- B.10.1. Makefile -- B.10.2. The Master Test Suite -- B.10.3. Test of AttrInfo -- B.10.4. Test of Dataset -- B.10.5. Test of Distance -- B.10.6. Test of nnMap -- B.10.7. Test of Matrices -- B.10.8. Test of Schema -- C. Software -- C.1. An Introduction to Makefiles -- C.1.1. Rules -- C.1.2. Variables -- C.2. Installing Boost -- C.2.1. Boost for Windows -- C.2.2. Boost for Cygwin or Linux -- C.3. Installing Cygwin -- C.4. Installing GMP -- C.5. Installing MPICH2 and Boost MPI.
• Publisher Details: Boca Raton, Fla : CRC Press
• Publication Date: 2011
• Copyright Date: 2011
• Extent: 1 online resource (xvii, 496 pages), illustrations
• Subjects: 006.312; Data mining; Cluster analysis; Relational databases; C++ (Computer program language); C++ (Computer program language); Cluster analysis; Data mining; Relational databases; Electronic books
• Languages: English
• ISBNs: 9781439862247; 1439862249
• Notes: Note: Includes bibliographical references (pages 469-486) and indexes.; Note: Print version record.
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