*Edited by Niansheng Tang*

In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.

*Andries Engelbrecht, Artificial Intelligence Series Editor*

ISBN 978-1-83969-887-3

Data Clustering

ISSN 2633-1403

Published in London, UK © 2022 IntechOpen © your\_photo / iStock

IntechOpen Series Artificial Intelligence, Volume 10
