Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Topology representing networks
Neural Networks
Integrating robust clustering techniques in S-PLUS
Computational Statistics & Data Analysis
Displaying a clustering with CLUSPLOT
Computational Statistics & Data Analysis
Programming with Data: A Guide to the S Language
Programming with Data: A Guide to the S Language
Thesis: clustering and instance based learning in first order logic
AI Communications
Short communication: Optimising k-means clustering results with standard software packages
Computational Statistics & Data Analysis
Dealing with label switching in mixture models under genuine multimodality
Journal of Multivariate Analysis
Neighborhood graphs, stripes and shadow plots for cluster visualization
Statistics and Computing
Pistis: A Privacy-Preserving Content Recommender System for Online Social Communities
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Digital divide across the European Union
Information and Management
Visitors of two types of museums: A segmentation study
Expert Systems with Applications: An International Journal
Bagged Clustering and its application to tourism market segmentation
Expert Systems with Applications: An International Journal
A fast partitioning algorithm and its application to earthquake investigation
Computers & Geosciences
A modification of the DIRECT method for Lipschitz global optimization for a symmetric function
Journal of Global Optimization
Asymmetric clustering using the alpha-beta divergence
Pattern Recognition
Music and timbre segmentation by recursive constrained K-means clustering
Computational Statistics
| Hi-index | 0.03 |
A methodological and computational framework for centroid-based partitioning cluster analysis using arbitrary distance or similarity measures is presented. The power of high-level statistical computing environments like R enables data analysts to easily try out various distance measures with only minimal programming effort. A new variant of centroid neighborhood graphs is introduced which gives insight into the relationships between adjacent clusters. Artificial examples and a case study from marketing research are used to demonstrate the influence of distances measures on partitions and usage of neighborhood graphs.