A new cluster tendency assessment method for fuzzy co-clustering in hyperspectral image analysis

Analysis of Graphic Perception Education for Young Children Based on Fuzzy Clustering Analysis

Geometric ability includes elements of identification, conceptualization, combination, drawing, and reasoning, and graphic perception is an important part of it. Kindergarten science education includes geometry instruction. Children are guided to perceive the relationship between shapes and figures through direct perception, first-hand experience, and practical operation through concentrated educational activities, and form image-concrete thinking over time, enhancing their perception and experience of the relationship between shapes in the objective world, and accumulating certain mathematical perceptual experience. Clustering is a branch of unsupervised pattern recognition that is very useful. Fuzzy clustering, which establishes the uncertainty description of samples to categories and can objectively reflect the real world, has become the mainstream of cluster analysis research. The graphics perception education of children is investigated using fuzzy clustering analysis. The main topic of this paper is how to apply children's graphics to the design of children's educational institutions and open up new creative perspectives for the design of children's educational institutions. The method of graphic perception education: perception education for preschool children is proposed based on the multichannel characteristics of preschool children's aesthetic perception and with reference to the theory of perception. The experimental results show that the improved algorithm reduces segmentation time by 171.48 s when compared to the traditional FCM algorithm for both noisy and high-quality images and that it is significantly faster than the FCM algorithm in terms of segmentation speed. As a result, the model construction of a set of children's graphic perception education for the cognitive characteristics of the age group children can provide corresponding references and references for related topic research.

Clustering based on adaptive local density with evidential assigning strategy

A new clustering algorithm, based on Adaptive Local Density (ALD) and Evidential K-Nearest Neighbors (EKNN), is proposed here. In density peaks clustering, many other density metrics fail to detect cluster centers on multi-density datasets, however the ALD deals with the tasks very well since it can better utilize the local information. To assign the remaining points after detecting the cluster centers, an assigning strategy in the framework of evidential theory, named EKNN, is created. The advantage of EKNN is twofold. Firstly, by fusing the information of K-Nearest Neighbors, it can reduce the risk of a phenomenon named domino effect: the drawback of one classical clustering, i.e., clustering by fast search and find of density peaks (always named as DPC). Secondly, it can detect border and noise points simultaneously since a credal partition is derived which can mine ambiguity and uncertainty of data structure. Simulations on both synthetic and real-world datasets demonstrate the outstanding performance of ALD-EKNN compared with DPC and some of its successors.