Algorithms for clustering data
Algorithms for clustering data
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction
IEEE Transactions on Image Processing
An incremental manifold learning algorithm based on the small world model
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
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Isomap is an important dimension reduction method for clustering data with relatively large features. Isomap uses geodesic distance instead of Euclidean distance to reflect geometry of the underlying manifold, while it ignores the classification principle that the distance between samples on different manifolds should be large and the distance between samples on the same manifold should be small. In this paper, we employed a path based distance to extend Isomap for clustering. The path based distance measure strengthens the similarity of the points on the same manifold. The useful behavior of the similarity strengthening Isomap is confirmed through numerical experiments with several data sets.