Online Clustering Algorithms
نویسندگان
چکیده
We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets.
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عنوان ژورنال:
- International journal of neural systems
دوره 18 3 شماره
صفحات -
تاریخ انتشار 2008