The only thorough, comprehensive book available on clustering
From two of the best–known experts in the field comes the first book to take a truly comprehensive look at clustering. The book begins with a complete introduction to cluster analysis in which readers will become familiarized with classification and clustering; definition of clusters; clustering applications; and the literature of clustering algorithms. The authors then present a detailed outline of the book′s content and go on to explore:
Neural network–based clustering
Sequential data clustering
Large–scale data clustering
Data visualization and high–dimensional data clustering
The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds. The book is intended as a professional reference for computer scientists and applied mathematicians working with data–intensive applications, and for computational intelligence researchers who use clustering for feature selection or data reduction. Its selection of homework exercises also makes it appropriate as a textbook for graduate students in mathematics, science, and engineering.
1. CLUSTER ANALYSIS.
1.1. Classifi cation and Clustering.
1.2. Defi nition of Clusters.
1.3. Clustering Applications.
1.4. Literature of Clustering Algorithms.
1.5. Outline of the Book.
2. PROXIMITY MEASURES.
2.2. Feature Types and Measurement Levels.
2.3. Defi nition of Proximity Measures.
2.4. Proximity Measures for Continuous Variables.
2.5. Proximity Measures for Discrete Variables.
2.6. Proximity Measures for Mixed Variables.
3. HIERARCHICAL CLUSTERING.
3.2. Agglomerative Hierarchical Clustering.
3.3. Divisive Hierarchical Clustering.
3.4. Recent Advances.
4. PARTITIONAL CLUSTERING.
4.2. Clustering Criteria.
4.3. K–Means Algorithm.
4.4. Mixture Density–Based Clustering.
4.5. Graph Theory–Based Clustering.
4.6. Fuzzy Clustering.
4.7. Search Techniques–Based Clustering Algorithms.
5. NEURAL NETWORK BASED CLUSTERING.
5.2. Hard Competitive Learning Clustering.
5.3. Soft Competitive Learning Clustering.
6. KERNEL–BASED CLUSTERING.
6.2. Kernel Principal Component Analysis.
6.3. Squared–Error–Based Clustering with Kernel Functions.
6.4. Support Vector Clustering.
7. SEQUENTIAL DATA CLUSTERING.
7.2. Sequence Similarity.
7.3. Indirect Sequence Clustering.
7.4. Model–Based Sequence Clustering.
7.5. Applications Genomic and Biological Sequence.
8. LARGE–SCALE DATA CLUSTERING.
8.2. Random Sampling Methods.
8.3. Condensation–Based Methods.
8.4. Density–Based Methods.
8.5. Grid–Based Methods.
8.6. Divide and Conquer.
8.7. Incremental Clustering.
9. DATA VISUALIZATION AND HIGH–DIMENSIONAL DATA CLUSTERING.
9.2. Linear Projection Algorithms.
9.3. Nonlinear Projection Algorithms.
9.4. Projected and Subspace Clustering.
10. CLUSTER VALIDITY.
10.2. External Criteria.
10.3. Internal Criteria.
10.4. Relative Criteria.
11. CONCLUDING REMARKS.