With the rapid development of information technology, data exhibit multi-view characteristics, and particularly single-view data cannot comprehensively describe the information of all examples. It is very significant ...
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ISBN:
(纸本)9781538695944
With the rapid development of information technology, data exhibit multi-view characteristics, and particularly single-view data cannot comprehensively describe the information of all examples. It is very significant to sufficiently make good use of the information from different views. A good multi-view learning strategy may lead to performance improvements. Multi-view clustering is one of the important branches in multi-view learning. The key problem is how to use information effectively from multiple different views, so as to discover the underlying structure of data more accurately. In general, it uses the complementary information available to improve clustering performance in multiple views. Meanwhile, it needs to ensure that the clustering results are consistent. In this paper, we review a number of representative multi-view clustering approaches in the different fields, which can be classified into four groups: cooperative style approaches, graph-based style approaches, multiplekernel learning-based approaches, and subspace learning-based approaches. Therefore, we try to summarize and analyze the development of multi-view clustering. Finally, we point out some specific challenges that are expected to improve further research in this rapidly developing field.
This paper deals with a Genetic multiplekernel Interval Type 2 Fuzzy C-means clustering (GMKIT2-FCM), which automatically find the optimal number of clusters and determine the coefficients of the multiplekernel. The...
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ISBN:
(纸本)9781467364690
This paper deals with a Genetic multiplekernel Interval Type 2 Fuzzy C-means clustering (GMKIT2-FCM), which automatically find the optimal number of clusters and determine the coefficients of the multiplekernel. The proposed GMKIT2-FCM algorithm provides us a new flexible vehicle to fuse different data information in the classification problems. That is, different information represented by different kernels is combined in the kernel space to produce a new kernel. The proposed algorithm contains two main stages. The first, a heuristic method based on Genetic algorithm (GA) and the average multiplekernel interval type 2 fuzzy c-means clustering (MKIT2-FCM) is adopted to automatically determine the optimal number of clusters and the initial the centroids. Then the results of the first stage are used in combination with GA and MKIT2-FCM to adjust the coefficients of multiplekernel to achieve better results. The experiments are done based on well-known datasets with the statistics show that the algorithm generates good quality of clustering problems.
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