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Clustering is an important approach in fault diagnosis. The dominant sets algorithm is a graph-based clustering algorithm, which defines the dominant set as a concept of a cluster. In this paper, we make an in-depth investigation of the dominant sets algorithm. As a result, we find that this algorithm is dependent on the similarity parameter in constructing the pairwise similarity matrix, and has...
Although numerous clustering algorithms can be found in literature, most of existing algorithms require one or more parameters as input, and their clustering performance usually depends heavily on user-specified parameters. Although some methods have been proposed to determine these parameters automatically, the parameter-tuning problem is still open in general. As a graph-theoretic approach to clustering,...
Clustering is an important unsupervised learning approach and widely used in pattern recognition, data mining and image processing, etc. Different from existing clustering algorithms based on partitioning within data, dominant sets clustering extracts clusters in a sequential fashion. Based on graph-theoretic concept of a cluster, dominant sets clustering can be accomplished with a game dynamics efficiently...
This paper focuses on the problem of achieving optimal transmit beamforming for a collection of wireless sensors. The objective is to design distributed algorithms running on each sensor (each carrying an antenna) such that the signals from all sensors combine constructively at the receiver end and thus, the received power is increased. Towards this goal, we propose two algorithms that are applicable...
This paper considers the multi-agent consensus problem for first-order discrete-time dynamic systems. Instead of utilizing the relative state information as a feedback, the state of every individual agent is updated based on the relative information between two groups, where the agents are randomly partitioned into two groups each time. In this paper, we consider the group information as the convex...
This paper presents a control and coordination algorithm for a group of autonomous agents to find the optimal path in a partially known environment. The novel control law utilizes the simulated annealing algorithm to search for the optimal path avoiding obstacles and threats or being trapped into the local values, but converging to the global optimal path with probability 1. We also show the multi-agent...
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