Multi-way partitioning of an undirected weighted graph where pairwise similarities are assigned as edge weights, provides an important tool for data clustering, but is an NP-hard problem. Spectral relaxation is a popular way of relaxation, leading to spectral clustering where the clustering is performed by the eigen-decomposition of the (normalized) graph Laplacian. On the other hand, semidefin...

In recent years it has been shown that clustering and segmentation methods can greatly benefit from the integration of prior information in terms of must-link constraints. Very recently the use of such constraints has been integrated in a rigorous manner also in graph-based methods such as normalized cut. On the other hand spectral clustering as relaxation of the normalized cut has been shown t...

In this paper, we propose an accelerated spectral clustering method, using a landmark selection strategy. According to the weighted PageRank algorithm, the most important nodes of the data affinity graph are selected as landmarks. The selected landmarks are provided to a landmark spectral clustering technique to achieve scalable and accurate clustering. In our experiments with two benchmark fac...

Clustering is a fundamental problem in machine learning with numerous important applications in statistical signal processing, pattern recognition, and computer vision, where unsupervised analysis of data classification structures are required. The current stateof-the-art in clustering is widely accepted to be the socalled spectral clustering. Spectral clustering, based on pairwise affinities o...

The traditional approach of classifying multispectral and hyperspectral imagery begins with clustering in feature space, followed by labeling the classes in the image space. To overcome some of the disadvantages of this sequential approach is to consider spatial constraints during clustering, for example by using Markov Random Fields. We propose in this paper an agent based clustering method as...

Data clustering is an important technique for visual data management. Most previous work focuses on clustering video data within single sources. In this paper, we address the problem of clustering across sources, and propose novel spectral clustering algorithms for multisource clustering problems. Spectral clustering is a new discriminative method realizing clustering by partitioning data graph...

Interval-valued data can find their practical applications in such situations as recording monthlyinterval temperatures at meteorological stations, daily interval stock prices, etc. The primary objectiveof the presented paper is to compare three different methods of fuzzy clustering for interval-valuedsymbolic data, i.e.: fuzzy c-means clustering, adaptive fuzzy c-means clustering a...

Spectral clustering and cloud computing is emerging branch of computer science or related discipline. It overcome the shortcomings of some traditional clustering algorithm and guarantee the convergence to the optimal solution, thus have to the widespread attention. This article first introduced the parallel spectral clustering algorithm research background and significance, and then to Hadoop t...

Reconstruction based subspace clustering methods compute a self reconstruction matrix over the samples and use it for spectral clustering to obtain the final clustering result. Their success largely relies on the assumption that the underlying subspaces are independent, which, however, does not always hold in the applications with increasing number of subspaces. In this paper, we propose a nove...