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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.
Development of Data Clustering
Clustering Algorithms: An Exploratory Review
Clustering by Similarity of Brazilian Legal Documents Using Natural Language Processing Approaches
Assessing Heterogeneity of Two-Part Model via Bayesian Model-Based Clustering with Its Application to Cocaine Use Data
Application of Jump Diffusion Models in Insurance Claim Estimation
Fuzzy Perceptron Learning for Non-Linearly Separable Patterns
Semantic Map: Bringing Together Groups and Discourses