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This paper presents two incremental clustering algorithms based on FCMK, a fuzzy clustering with multiple kernels algorithm we developed earlier [1]. The FCMK algorithm has a memory requirement of O(N2), where N is the number of objects in the data set. Thus, even data sets that have nearly 1, 000, 000 objects require terabytes of working memory-impractical for most computers. One way to attack this...
While classical kernel-based clustering algorithms are based on a single kernel, in practice it is often desirable to base clustering on combination of multiple kernels. In [1], we considered a fuzzy c-means with multiple kernels in observation space (FCMK-OS) algorithm which constructs the kernel from a number of Gaussian kernels and learns a resolution specific weight for each kernel function in...
In this paper, the relational fuzzy c-means clustering algorithm is extended to an adaptive cluster model which maps data points to a high dimensional feature space through an optimal convex combination of homogenous kernels with respect to each cluster. This generalized model, called Relational Fuzzy C-Means with Multiple Kernels (RFCM-MK), strives to find a good partitioning of the data into meaningful...
Recognizing avatars in virtual worlds is a very important issue for law enforcement agencies, terrorism and security experts. In this paper, a novel face recognition technique based on wavelet transform and Hierarchical Multi-scale Local Binary Pattern (HMLBP) is presented and shown to increase the accuracy of recognition of avatar faces. The proposed technique consists of three stages: preprocessing,...
In this paper, the kernel fuzzy c-means clustering algorithm is extended to an adaptive cluster model which maps data points to a high dimensional feature space through an optimal convex combination of homogenous kernels with respect to each cluster. This generalized model, called Fuzzy C-Means with Multiple Kernels (FCM-MK), strives to find a good partitioning of the data into meaningful clusters...
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