Clustering in Bioinformatics and Drug Discovery

Introduction History Bioinformatics and Drug Discovery Statistical Learning Theory and Exploratory Data Analysis Clustering Algorithms Computational Complexity Data Types Normalization and Scaling Transformations Formats Data Matrices Measures of Similarity Proximity Matrices Symmetric Matrices Dimensionality, Components, Discriminants Graph Theory Clustering Forms Partitional Hierarchical Mixture Models Sampling Overlapping Fuzzy Self-Organizing Hybrids Partitional Algorithms K-Means Jarvis-Patrick Spectral Clustering Self-Organizing Maps Cluster Sampling Algorithms Leader Algorithms Taylor-Butina Algorithm Hierarchical Algorithms Agglomerative Divisive Hybrid Algorithms Self-Organizing Tree Algorithm Divisive Hierarchical K-Means Exclusion Region Hierarchies Biclustering Asymmetry Measures Algorithms Ambiguity Discrete Valued Data Types Precision Ties in Proximity Measure Probability and Distributions Algorithm Decision Ambiguity Overlapping Clustering Algorithms Based on Ambiguity Validation Validation Measures Visualization Example Large Scale and Parallel Algorithms Leader and Leader-Follower Algorithms Taylor-Butina K-Means and Variants Examples Appendices Bibliography A Glossary and Exercises appear at the end of each chapter.

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• Farmacologia
• Bioinformática medica
• Biologia, ciências da vida