In the future robots will have to operate autonomously for long periods of time. To achieve this they need to be able to learn directly from their environment without human supervision. The use of clustering methods is one possibility to tackle this challenge. Here we present extensions to affinity propagation, a clustering algorithm proposed by Frey and Dueck [5], which makes it suitable for r...

Affinity propagation clustering (AP) has two limitations: it is hard to know what value of parameter ‘preference’ can yield an optimal clustering solution, and oscillations cannot be eliminated automatically if occur. The adaptive AP method is proposed to overcome these limitations, including adaptive scanning of preferences to search space of the number of clusters for finding the optimal clus...

As information technology is developing rapidly, massive and high dimensional data sets have appeared in abundance. The existing attribute reduction methods are encountering bottleneck problem of timeliness and spatiality. AP(Affinity Propagation) is an efficient and fast clustering algorithm for large dataset compared with the existing clustering algorithms. This paper discusses attribute clus...

We describe VarClust, a gossip-based decentralized clustering algorithm designed to support multi-mean decentralized aggregation in energy-constrained wireless sensor networks. We empirically demonstrate thatVarClust is at least as accurate as, and requires less node-to-node communication (and hence consumes less energy) than, a state-of-the-art aggregation approach, affinity propagation. This ...

Beyond Affinity Propagation: Message Passing Algorithms for Clustering Inmar-Ella Givoni Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2012 Affinity propagation is an exemplar-based clustering method that takes as input similarities between data points. It outputs a set of data points that best represent the data (exemplars), and assignments of each non-exem...

In this paper, we address a problem of managing tagged images with hybrid summarization. We formulate this problem as finding a few image exemplars to represent the image set semantically and visually, and solve it in a hybrid way by exploiting both visual and textual information associated with images. We propose a novel approach, called homogeneous and heterogeneous message propagation (HMP)....