Mikko I. Malinen

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We present a k-means-based clustering algorithm, which optimizes mean square error, for given cluster sizes. A straightforward application is balanced clustering, where the sizes of each cluster are equal. In k-means assignment phase, the algorithm solves the assignment problem by Hungarian algorithm. This is a novel approach, and makes the assignment phase(More)
Data clustering is a combinatorial optimization problem. This article shows that clustering is also an optimization problem for an analytic function. The mean squared error, or in this case, the squared error can expressed as an analytic function. With an analytic function we benefit from the existence of standard optimization methods: the gradient of this(More)
It is difficult to apply traditional Minimum spanning tree(MST) algorithms to a large dataset since the time complexity of the algorithms is quadratic. In this paper, we present a fast approximate MST framework on the complete graph of N points, and any exact MST algorithm can be incorporated into the framework and speeded up. The proposed framework employs(More)
Traditional approach to clustering is to fit a model (partition or prototypes) for the given data. We propose a completely opposite approach by fitting the data into a given clustering model that is optimal for similar pathological data of equal size and dimensions. We then perform inverse transform from this synthetic data back to the original data while(More)
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