Online unit clustering is a clustering problem where classification of points is done in an online fashion, but the exact location of clusters can be modified dynamically. We stud...
Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with ...
Maurizio Filippone, Francesco Camastra, Francesco ...
Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future, due to both technological and social/commercial reasons. From the data mining vie...
Many clustering algorithms have been proposed to partition a set of static data points into groups. In this paper, we consider an evolutionary clustering problem where the input d...
In distributed data mining models, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose...
eresting web-available abstracts and papers on clustering: An Analysis of Recent Work on Clustering Algorithms (1999), Daniel Fasulo : This paper describes four recent papers on cl...
We use cluster analysis as a unifying principle for problems from low, middle and high level vision. The clustering problem is viewed as graph partitioning, where nodes represent ...
Abstract. We propose a novel approach to clustering, based on deterministic analysis of random walks on the weighted graph associated with the clustering problem. The method is cen...
We propose a new formulation of the clustering problem that differs from previous work in several aspects. First, the goal is to explicitly output a collection of simple and meani...
The complexity, approximation and algorithmic issues of several clustering problems are studied. These non-traditional clustering problems arise from recent studies in microarray ...