Data Clustering: Theory, Algorithms, and Applications
Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, centre-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Suitable as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
ACM Press attribute binary categorical data cell centroid Chapter cluster analysis cluster centers Cluster ID clustering algorithms coefficient criterion d-dimensional data clustering data mining data points data set databases defined in equation dendrogram denote density dissimilarity matrix dissimilarity measure dTemp EM algorithm Euclidean distance example Figure fuzzy clustering gene expression gene expression data given graph hierarchical algorithms hierarchical clustering high-dimensional IEEE Computer Society IEEE Transactions initial input international conference iteration Journal k-means algorithm k-modes algorithm kd-tree knowledge discovery M-file MATLAB medoid membership merged minimized missing values model-based clustering node number of clusters number of data Number of genes nv Ship objective function optimal parameters Pattern Recognition procedure Proceedings proposed random sample Section similarity measure space Statistical subspace clustering Table tabu tabu search techniques Theorem updated variable vector visualization x1 x2 x n