Extract the network communities based on fuzzy clustering theory

Community detection in complex networks is of great importance in analyzing the interaction patterns and group behaviors. However, the traditional method of community division divide each node in the network into a specific community, while may ignore its internal connection. In this paper, a new strategy that selects a fuzzy function and fuzzy threshold (FF-FT) was presented to discover community structure. Edge dense degree coefficient was introduced to calculate fuzzy relation between nodes, and Fast–Warshall algorithm was used to reduce the complexity of FF-FT. Through the theoretical analysis and the comparison of eight current well-known community detection algorithms on seven real networks and artificial networks with different parameters, the results show that the FF-FT algorithm has a good community detection performance.

Binarization Segmentation Based on Fuzzy Clustering Theory

Aiming at the problem of color image two value segmentation, present an improved two value plant image segmentation method based on fuzzy clustering theory. In the light of the problem that fuzzy clustering algorithm is difficult to segment the low SNR image, improve Membership function; Aiming at the problem that image pixel information is too large, mapping image information to the gray feature space, treat the same gray pixel as a whole, improve operation efficiency. The experimental results show that, the improved algorithm has stronger anti noise ability, higher segmentation accuracy, shorter operation time, has good prospects for engineering applications.

Adaptive Tracking Algorithm of Multi-Target Based on Fuzzy Clustering

For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.