Algorithms for clustering data
Algorithms for clustering data
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
An experimental comparison of model-based clustering methods
Machine Learning
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
An overview of clustering methods
Intelligent Data Analysis
Robust partitional clustering by outlier and density insensitive seeding
Pattern Recognition Letters
A robust iterative refinement clustering algorithm with smoothing search space
Knowledge-Based Systems
A Mean Shift-Based Initialization Method for K-means
CIT '12 Proceedings of the 2012 IEEE 12th International Conference on Computer and Information Technology
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Robustness is an important concept when dealing with clustering algorithms. While most literature directed to this concept discusses robustness with respect to changes in the given data set, this paper focuses on robustness with respect to changes in the initial conditions. We build on our previous work, where we introduced the concepts of instability and cluster stability variance to measure the robustness in terms of initial conditions. Results from previous work are extended to a much broader class of clusterings, and we introduce the notion of structure-preserving data element. It is proven that removing a structure-preserving unstable data element from the data set increases the robustness of the considered clustering algorithm, as measured by its instability, while the structure of the given data set is conserved. The practical significance of detecting structure-preserving unstable data elements is also discussed.