A tandem clustering process for multimodal datasets

A tandem clustering process for multimodal datasets

0.00 Avg rating0 Votes
Article ID: iaor20063702
Country: Netherlands
Volume: 168
Issue: 3
Start Page Number: 998
End Page Number: 1008
Publication Date: Feb 2006
Journal: European Journal of Operational Research
Authors: , , ,
Keywords: artificial intelligence
Abstract:

Clustering multimodal datasets can be problematic when a conventional algorithm such as k-means is applied due to its implicit assumption of Gaussian distribution of the dataset. This paper proposes a tandem clustering process for multimodal data sets. The proposed method first divides the multimodal dataset into many small pre-clusters by applying k-means or fuzzy k-means algorithm. These pre-clusters are then clustered again by agglomerative hierarchical clustering method using Kullback–Leibler divergence as an initial measure of dissimilarity. Benchmark results show that the proposed approach is not only effective at extracting the multimodal clusters but also efficient in computational time and relatively robust at the presence of outliers.

Reviews

Required fields are marked *. Your email address will not be published.