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Table of Contents
I. Introduction
II. Clustering Algorithms
A. Distance and Similarity Measures
B. Hierarchical Clustering
C. Squared Error-Based (Vector Quantization)
D. pdf Estimation via Mixture Densities
E. Graph Theory-Based
F. Combinatorial Search Techniques-Based
G. Fuzzy
H. Neural Networks-Based
I. Kernel-Based
J. Sequential Data
K. Large-Scale Data Sets
L. Data visualization and High-dimensional Data
M. How Many Clusters?
Summary
This paper presents a comprehensive overview of influential clustering algorithms rooted in statistics, computer science, and machine learning. It discusses various proximity measures, hierarchical and partitional clustering methods, and advanced techniques like fuzzy clustering, neural networks-based clustering, and kernel-based clustering. The paper also delves into applications of clustering algorithms in bioinformatics and other fields. The authors provide detailed insights into how to determine the appropriate number of clusters in different data sets. Overall, the paper serves as a systematic guide to clustering algorithms and their applications.