OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Independent component analysis: algorithms and applications
Neural Networks
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
The complexity of computing the MCD-estimator
Theoretical Computer Science
Semi-Supervised Adapted HMMs for Unusual Event Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Novel Approach to Noise Clustering for Outlier Detection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
Neural Networks
A genetic approach for efficient outlier detection in projected space
Pattern Recognition
Towards Better Outliers Detection for Gene Expression Datasets
BIOTECHNO '08 Proceedings of the 2008 International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies
Robust fuzzy clustering using mixtures of Student's-t distributions
Pattern Recognition Letters
Outlier identification and market segmentation using kernel-based clustering techniques
Expert Systems with Applications: An International Journal
Projected outlier detection in high-dimensional mixed-attributes data set
Expert Systems with Applications: An International Journal
Robust probabilistic PCA with missing data and contribution analysis for outlier detection
Computational Statistics & Data Analysis
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Enhancing minimum spanning tree-based clustering by removing density-based outliers
Digital Signal Processing
Mutual information evaluation: A way to predict the performance of feature weighting on clustering
Intelligent Data Analysis
| Hi-index | 12.05 |
Unsupervised clustering for datasets with severe outliers inside is a difficult task. In this approach, we propose a cluster-dependent multi-metric clustering approach which is robust to severe outliers. A dataset is modeled as clusters each contaminated by noises of cluster-dependent unknown noise level in formulating outliers of the cluster. With such a model, a multi-metric Lp-norm transformation is proposed and learnt which maps each cluster to the most Gaussian distribution by minimizing some non-Gaussianity measure. The approach is composed of two consecutive phases: multi-metric location estimation (MMLE) and multi-metric iterative chi-square cutoff (ICSC). Algorithms for MMLE and ICSC are proposed. It is proved that the MMLE algorithm searches for the solution of a multi-objective optimization problem and in fact learns a cluster-dependent multi-metric Lq-norm distance and/or a cluster-dependent multi-kernel defined in data space for each cluster. Experiments on heavy-tailed alpha-stable mixture datasets, Gaussian mixture datasets with radial and diffuse outliers added respectively, and the real Wisconsin breast cancer dataset and lung cancer dataset show that the proposed method is superior to many existent robust clustering and outlier detection methods in both clustering and outlier detection performances.