In graph clustering methods, MinMax Cut tends to provide more balanced clusters as compared to Ratio Cut and Normalized Cut. The traditional approach used spectral relaxation to solve the graph cut problem. The main disadvantage of this approach is that the obtained spectral solution has mixed signs, which could severely deviate from the true solution and have to resort to other clustering meth...

Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the standard graph-based clustering methods. Existing methods for the computation of multiple clusters, corresponding to a balanced k-cut of the graph, are either based on greedy techniques or heuristics which have weak connection to the original motivation of minimizing the normalized cut. In this paper we propos...

Image Segmentation is an important image processing technique which is used to analyse colour, texture etc. Image Segmentation is used to separate an image into several “meaningful” parts. Normalized cut (Ncut) is based on graph cut technique to solve the image Segmentation problems. Rather than just focusing on local features and their consistencies, Ncut consider the global impression of an i...

We try to extend the seeding technique proposed in [2],to the spectral clustering domain.We start with the survey of a sequence of papers on the connection between spectral clustering techniques and centroid-based algorithms. We show that the results in [2] can provide a bound for the rounding of the normalized cut problem. This raises an interesting question on the relationship between the see...

This paper presents an application of a hierarchical social (HS) metaheuristic to region-based segmentation. The original image is modelled as a simplified image graph, which is successively partitioned into two regions, corresponding to the most significant components of the actual image, until a termination condition is met. The graph-partitioning task is solved as a variant of the min-cut pr...

Normalized cut is a widely used measure of separation between clusters in a graph. In this paper we provide a novel probabilistic perspective on this measure. We show that for a partition of a graph into two weakly connected sets, V = A B, the multiway normalized cut is approximately MNcut ≈ 1/τA→B + 1/τB→A, where τA→B is the unidirectional characteristic exit time of a random walk from subset ...

We present in this paper several solutions to the challenging task of clustering software defect reports. Clustering defect reports can be very useful for prioritizing the testing effort and to better understand the nature of software defects. Despite some challenges with the language used and semi-structured nature of defect reports, our experiments on data collected from the open source proje...

This paper follows a word-document co-clustering model independently introduced in 2001 by several authors such as I.S. Dhillon, H. Zha and C. Ding. This model consists in creating a bipartite graph based on word frequencies in documents, and whose vertices are both documents and words. The created bipartite graph is then partitioned in a way that minimizes the normalized cut objective function...

Based on the current studies on the algorithms of the affinity propagation and normalized cut, a new scalable graph clustering method called APANC (Affinity Propagation And Normalized Cut) is proposed in this paper. During the APANC process, we firstly use the “Affinity Propagation” (AP) to preliminarily group the original data in order to reduce the data-scale, and then we further group the re...

With the exponential growth of information on the World Wide Web, there is great demand for developing e.cient methods for e/ectively organizing the large amount of retrieved information. Document clustering plays an important role in information retrieval and taxonomy management for the Web. In this paper we examine three clustering methods: K-means, multi-level METIS, and the recently develop...