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Distance metrics for categorical data play an important role in unsupervised learning such as clustering. They also dramatically affect learning accuracy and computational complexities. Recently, two co-occurrence methods, Co-occurrence Distance based on Power Set (CDPS) and Co-occurrence Distance based on Universal Set (CDUS), have been proposed to calculate distances for categorical attribute values...
Clustering is considered as the most important unsupervised learning problem. It aims to find some structure in a collection of unlabeled data. Dealing with a large quantity of data items can be problematic because of time complexity. On the other hand high dimensional data is a challenge arena in data clustering e.g. time series data. Novel algorithms are needed to be robust, scalable, efficient...
Perspective deformation is one of the main issues needed to be addressed in real-scene character recognition. An effective recognition approach, which is able to handle severe perspective deformation, is to employ cross ratio spectrum and dynamic time warping techniques. However, this solution suffers from a time complexity of O(n4). In this paper, a clustering based indexing method is proposed to...
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