Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demon
This chapter provides a review of the field of clustering and classification as applied in the behav...
Cluster analysis via a finite mixture model approach is considered. With this approach to clustering...
Clustering is a division of data into groups of similar objects. Representing the data by fewer clus...
Cluster analysis seeks to identify homogeneous subgroups of cases in a population. This article prov...
Cluster Analysis aims at finding subsets (clusters) of a given set of entities, which are homogeneou...
Abstract The chapter by Milligan and Hirtle provides an overview of the current state of knowledge i...
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research devel...
Clustering has become an increasingly popular method of multivariate analysis over the past two deca...
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research devel...
Clustering is one of the most useful tasks in data mining process for discovering groups and identif...
This book explains and illustrates the most frequently used methods of hierarchical cluster analysis...
Cluster Analysis for Applications deals with methods and various applications of cluster analysis. T...
From the wide ranging ‘Handbooks of modern statistical methods’ series, this book seeks to be a non‐...
Classical clustering methods usually work with a set of objects as statistical data units described ...
3rd SMTDA Conference Proceedings, 11-14 June 2014, Lisbon Portugal.Cluster analysis or "unsupervised...
This chapter provides a review of the field of clustering and classification as applied in the behav...
Cluster analysis via a finite mixture model approach is considered. With this approach to clustering...
Clustering is a division of data into groups of similar objects. Representing the data by fewer clus...
Cluster analysis seeks to identify homogeneous subgroups of cases in a population. This article prov...
Cluster Analysis aims at finding subsets (clusters) of a given set of entities, which are homogeneou...
Abstract The chapter by Milligan and Hirtle provides an overview of the current state of knowledge i...
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research devel...
Clustering has become an increasingly popular method of multivariate analysis over the past two deca...
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research devel...
Clustering is one of the most useful tasks in data mining process for discovering groups and identif...
This book explains and illustrates the most frequently used methods of hierarchical cluster analysis...
Cluster Analysis for Applications deals with methods and various applications of cluster analysis. T...
From the wide ranging ‘Handbooks of modern statistical methods’ series, this book seeks to be a non‐...
Classical clustering methods usually work with a set of objects as statistical data units described ...
3rd SMTDA Conference Proceedings, 11-14 June 2014, Lisbon Portugal.Cluster analysis or "unsupervised...
This chapter provides a review of the field of clustering and classification as applied in the behav...
Cluster analysis via a finite mixture model approach is considered. With this approach to clustering...
Clustering is a division of data into groups of similar objects. Representing the data by fewer clus...