Multi-Agent Behaviour Segmentation via Spectral Clustering
نویسندگان
چکیده
We examine the application of spectral clustering for breaking up the behaviour of a multi-agent system in space and time into smaller, independent elements. We extend the clustering into the temporal domain and propose a novel similarity measure, which is shown to possess desirable temporal properties when clustering multi-agent behaviour. We also propose a technique to add knowledge about events of multi-agent interaction with different importance. We apply spectral clustering with this measure for analysing behaviour in a strategic game.
منابع مشابه
A Multi-scale Segmentation Method of Oil Spills in SAR Images Based on JSEG and Spectral Clustering
Image segmentation is a key step of oil spills detection in SAR images. For the problem that the traditional multi-spectral clustering algorithm with the features extraction by GLCM (Gray-Level Co-occurrence Matrix) has such limitations as direction sensitivities and difficulties in selecting the best feature combination etc., this paper proposes a multi-scale segmentation method of oil spills ...
متن کاملMulti-level Low-rank Approximation-based Spectral Clustering for image segmentation
Spectral clustering is a well-known graph-theoretic approach of finding natural groupings in a given dataset, and has been broadly used in image segmentation. Nowadays, High-Definition (HD) images are widely used in television broadcasting and movies. Segmenting these high resolution images presents a grand challenge to the current spectral clustering techniques. In this paper, we propose an ef...
متن کاملMulti-criteria Logistic Hub Location by Network Segmentation under Criteria Weights Uncertainty (RESEARCH NOTE)
Third party service providers are locating logistic hub for operating their tasks. Finding a proper location helps them to have better performance in competitive environment. Multiple characteristics of proper location selection faces the decision maker to have a multi criteria decision making problem. Since the location decision is a long term planning, the robustness of the decision is gettin...
متن کاملKernel Spectral Clustering and applications
In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane i...
متن کاملStudy on multi-center fuzzy C-means algorithm based on transitive closure and spectral clustering
Fuzzy C-means (FCM) clustering has been widely used successfully in many real-world applications. However, the FCM algorithm is sensitive to the initial prototypes, and it cannot handle non-traditional curved clusters. In this paper, a multi-center fuzzy C-means algorithm based on transitive closure and spectral clustering (MFCM-TCSC) is provided. In this algorithm, the initial guesses of the l...
متن کامل