Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
An ISODATA algorithm for straight line fitting
Pattern Recognition Letters
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Simultaneous fitting of several planes to point sets using neural networks
Computer Vision, Graphics, and Image Processing
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Clustering in massive data sets
Handbook of massive data sets
Design of hybrids for the minimum sum-of-squares clustering problem
Computational Statistics & Data Analysis
Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable
Computational Statistics & Data Analysis
Finding approximate solutions to combinatorial problems with very large data sets using BIRCH
Computational Statistics & Data Analysis
Robust clusterwise linear regression through trimming
Computational Statistics & Data Analysis
Sensor Fusion for SLAM Based on Information Theory
Journal of Intelligent and Robotic Systems
A class of fuzzy clusterwise regression models
Information Sciences: an International Journal
Service time estimation with a refinement enhanced hybrid clustering algorithm
ASMTA'10 Proceedings of the 17th international conference on Analytical and stochastic modeling techniques and applications
Exploring the number of groups in robust model-based clustering
Statistics and Computing
Indirect estimation of service demands in the presence of structural changes
Performance Evaluation
Robust clustering around regression lines with high density regions
Advances in Data Analysis and Classification
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A new method to detect different linear structures in a data set, called Linear Grouping Algorithm (LGA), is proposed. LGA is useful for investigating potential linear patterns in data sets, that is, subsets that follow different linear relationships. LGA combines ideas from principal components, clustering methods and resampling algorithms. It can detect several different linear relations at once. Methods to determine the number of groups in the data are proposed. Diagnostic tools to investigate the results obtained from LGA are introduced. It is shown how LGA can be extended to detect groups characterized by lower dimensional hyperplanes as well. Some applications illustrate the usefulness of LGA in practice.