Clustering is a data analysis technique, particularly useful when there are many dimensions and little prior information about the data. Partitional clustering algorithms are efficient, but suffer from sensitivity to the initial partition and noise. We propose here k-Attractors, a partitional clustering algorithm tailored to numeric data analysis. As a pre-processing (initialization) step, it e...

Hierarchical clustering is an important technique for hierarchical data exploration applications. However, most existing hierarchial methods are based on traditional one-side clustering, which is not effective for handling high dimensional data. In this paper, we develop a partitional hierarchical co-clustering framework and propose a Hierarchical Information-Theoretical Co-Clustering (HITCC) a...

Identifying crosscutting concerns is an important issue in the maintenance of software systems. It aims at refactoring the existing systems to use aspect oriented programming, in order to make them easier to maintain and to evolve. In this paper we present a new partitional clustering algorithm for identifying crosscutting concerns in existing software systems. We experimentally evaluate our al...

This paper addresses a fundamental problem in ensemble clustering – namely, how should one compare the similarity of two clusterings? The vast majority of prior techniques for comparing clusterings are entirely partitional, i.e., they examine assignments of points in set theoretic terms after they have been partitioned. In doing so, these methods ignore the spatial layout of the data, disregard...

Clustering helps with reengineering by gathering the software entities into meaningful and independent groups. The entities here can be any FAMIX entities, be it classes, methods, attributes etc. The affinity between two entities is calculated through the absolute difference of their properties and properties of the dependencies between the two entities; all the properties also have assigned we...

Model-based clustering techniques have been widely used and have shown promising results in many applications involving complex data. This paper presents a unified framework for probabilistic model-based clustering based on a bipartite graph view of data and models that highlights the commonalities and differences among existing model-based clustering algorithms. In this view, clusters are repr...

This paper describes the recognition and location of 3D object models from depth-based data using a parallel pose clustering algorithm. We describe a leader-based partitional clustering algorithm and demonstrate a successful parallel implementation of this. Results are presented for a variety of synthetic and real scene data. We also consider how the basic approach can be extended to recognise ...

Keywords: Cluster analysis Fuzzy clustering Fuzzy c-means (FCM) Initialization Bias correction Probability weight a b s t r a c t Fuzzy clustering is generally an extension of hard clustering and it is based on fuzzy membership partitions. In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. Numerous studies have presented various generalizations o...

This paper presents partitional fuzzy clustering methods based on adaptive quadratic distances. The methods presented furnish a fuzzy partition and a prototype for each cluster by optimizing an adequacy criterion based on adaptive quadratic distances. These distances change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Moreover...