Mikko I. Malinen

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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)
Minimum spanning trees (MSTs) have long been used in data mining, pattern recognition and machine learning. However, it is difficult to apply traditional MST algorithms to a large dataset since the time complexity of the algorithms is quadratic. In this paper, we present a fast MST (FMST) algorithm on the complete graph of N points. The proposed algorithm(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)
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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