Porównanie stabilności zagregowanych algorytmów taksonomicznych opartych na idei metody bagging

• Authors: Dorota Rozmus

Title variants

EN

Comparison of Stability of Cluster Ensembles based on Bagging Idea

• Languages of publication: PL

Abstracts

EN

Ensemble approach has been successfully applied in the context of supervised learning to increase the accuracy and stability of classification. One of the most popular method is bagging based on bootstrap samples. Recently, analogous techniques for cluster analysis have been suggested in order to increase classification accuracy, robustness and stability of the clustering solutions. Research has proved that, by combining a collection of different clusterings, an improved solution can be obtained. A desirable quality of the method is the stability of a clustering algorithm with respect to small perturbations of data (e.g., data subsampling or resampling, small variations in the feature values) or the parameters of the algorithm (e.g., random initialization). Here, we look at the stability of the ensemble and carry out an experimental study to compare stability of cluster ensembles based on bagging idea.

Keywords

PL

Agregacja modeli; Algorytmy; Metody taksonomiczne

EN

Aggregation models; Algorithms; Taxonomic methods

• Publisher: Uniwersytet Ekonomiczny w Katowicach
• Journal: Studia Ekonomiczne
• Year: 2013
• Volume: 133
• Pages: 119-134

Contributors

author

Dorota Rozmus

References

• Bezdek J.C. (1981): Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York.
• Breiman L. (1996): Bagging Predictors. "Machine Learning", No. 26(2).
• Dudoit S., Fridlyand J. (2003): Bagging to Improve the Accuracy of a Clustering Procedure. "Bioinformatics", Vol. 19, No. 9.
• Fern X.Z., Brodley C.E. (2003): Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach. Proceedings of the 20th International Conference of Machine Learning.
• Fred A. (2002): Finding Consistent Clusters in Data Partitions. "Proceedings of the International Workshop on Multiple Classifier Systems".
• Fred N.L., Jain A.K. (2002): Combining Multiple Clusterings Using Evidence Accumulation. "IEEE Transactions on PAMI", No. 27(6).
• Freund Y. (1999): An Adaptive Version of the Boost by Majority Algorithm. Proceedings of the 12th Annual Conference on Computational Learning Theory.
• Hornik K. (2005): A CLUE for CLUster Ensembles. "Journal of Statistical Software", No. 14.
• Hubert L.., Arabie P. (1985): Evaluating Object Set Partitions: Free Sort Analysis and Some Generalizations. "Journal of Verbal Learning and Verbal Behaviour", No. 15.
• Kuncheva L., Vetrov D. (2006): Evaluation of Stability of k-means Cluster Ensembles with Respect to Random Initialization. "IEEE Transactions On Pattern Analysis And Machine Intelligence", Vol. 28, No. 11.
• Leisch F. (1999): Bagged Clustering. Adaptive Information Systems and Modeling in Economics and Management Science, Working Paper 51.
• Strehl A., Ghosh J. (2002): Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions. "Journal of Machine Learning Research", No. 3.

Identifiers

ISSN

2083-8611