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Title
Abstract
Introduction
Experimental Study and Discussion
Datasets
Empirical Results on Bagging Approaches
Bootstrap and Subsampling Sampling Methods
Empirical Study on Boosting Approach
References
All Topics
Computer Science
Machine Learning

Data Weighting Mechanisms for Clustering Ensembles

Profile image of Hamid Alinejad-RoknyHamid Alinejad-Rokny
https://doi.org/10.1016/J.COMPELECENG.2013.02.004
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Abstract

Inspired by bagging and boosting algorithms in classification, the non-weighing and weighing-based sampling approaches for clustering are proposed and studied in the paper. The effectiveness of non-weighing-based sampling technique, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms have been invested in this paper. Experimental results have shown improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and boosting techniques. Subsamples of small size can reduce the computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical research study also compares the performance of boosting and bagging clustering ensembles using different consensus functions on a number of datasets.

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