Ensembles based on random projections to improve the accuracy of clustering algorithms
作者： Alberto Bertoni , Giorgio Valentini
DOI: 10.1007/11731177_5
关键词:
摘要: We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtained. Multiple clusterings are performed random subspaces, approximately preserving the distances projected data, and then they combined using pairwise similarity matrix; in this way accuracy of each “base” clustering maintained, diversity them improved. The proposed approach effective problems characterized high dimensional as shown our preliminary experimental results.
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sci-hub.st 参考文章(10)
Giorgio Valentini, Francesco Masulli, Ensembles of Learning Machines italian workshop on neural nets. ,vol. 2486, pp. 3- 22 ,(2002) , 10.1007/3-540-45808-5_1
Thomas G. Dietterich, Ensemble Methods in Machine Learning Multiple Classifier Systems. pp. 1- 15 ,(2000) , 10.1007/3-540-45014-9_1
A. Bertoni, G. Valentini, Random projections for assessing gene expression cluster stability international joint conference on neural network. ,vol. 1, pp. 149- 154 ,(2005) , 10.1109/IJCNN.2005.1555821
Mark Smolkin, Debashis Ghosh, Cluster stability scores for microarray data in cancer studies. BMC Bioinformatics. ,vol. 4, pp. 36- 36 ,(2003) , 10.1186/1471-2105-4-36
Joe H. Ward, Hierarchical Grouping to Optimize an Objective Function Journal of the American Statistical Association. ,vol. 58, pp. 236- 244 ,(1963) , 10.1080/01621459.1963.10500845
Ella Bingham, Heikki Mannila, Random projection in dimensionality reduction: applications to image and text data knowledge discovery and data mining. pp. 245- 250 ,(2001) , 10.1145/502512.502546
Alexander Strehl, Joydeep Ghosh, Cluster ensembles --- a knowledge reuse framework for combining multiple partitions Journal of Machine Learning Research. ,vol. 3, pp. 583- 617 ,(2003) , 10.1162/153244303321897735
Stefan T. Hadjitodorov, Ludmila I. Kuncheva, Ludmila P. Todorova, Moderate diversity for better cluster ensembles Information Fusion. ,vol. 7, pp. 264- 275 ,(2006) , 10.1016/J.INFFUS.2005.01.008
Tin Kam Ho, The random subspace method for constructing decision forests IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 20, pp. 832- 844 ,(1998) , 10.1109/34.709601
G. Valentini, An experimental bias-variance analysis of SVM ensembles based on resampling techniques systems man and cybernetics. ,vol. 35, pp. 1252- 1271 ,(2005) , 10.1109/TSMCB.2005.850183