This paper introduces a polynomial time approximation scheme for the metric Correlation Clustering problem, when the number of clusters returned is bounded (by k). Consensus Clustering is a fundamental aggregation problem, with considerable application, and it is analysed here as a metric variant of the Correlation Clustering problem. The PTAS exploits a connection between Correlation Clusterin...

The main focus of this thesis concerns the further developments in the areas of ensemble and constrained clustering. The goal of the proposed methods is to address clustering problems, in which the optimal clustering method is unknown. Additionally, by means of pairwise linkage constraints, it is possible to aggregate extra information to the clustering framework. Part I investigates the concep...

Consensus clustering has emerged as one of the principal clustering problems in the data mining community. In recent years the theoretical computer science community has generated a number of approximation algorithms for consensus clustering and similar problems. These algorithms run in polynomial time, with performance guaranteed to be at most a certain factor worse than optimal. We investigat...

MOTIVATION Hierarchical and relocation clustering (e.g. K-means and self-organizing maps) have been successful tools in the display and analysis of whole genome DNA microarray expression data. However, the results of hierarchical clustering are sensitive to outliers, and most relocation methods give results which are dependent on the initialization of the algorithm. Therefore, it is difficult t...

Clustering ensemble has emerged as an important extension of the classical clustering problem. It provides a framework for combining multiple base clusterings of a data set to generate a final consensus result. Most existing clustering methods simply combine clustering results without taking into account the noises, which may degrade the clustering performance. In this paper, we propose a novel...

Bagging and boosting are two successful well-known methods for developing classifier ensembles. It is recognized that the clusterer ensemble methods which utilize the boosting concept, can create clusterings with quality and robustness improvement. In this paper, we introduce a new boosting based hierarchical clusterer ensemble method called Bob-Hic. This method is utilized to create a consensu...

Cluster Ensembles is a framework for combining multiple partitionings obtained from separate clustering runs into a final consensus clustering. This framework has attracted much interest recently because of its numerous practical applications, and a variety of approaches including Graph Partitioning, Maximum Likelihood, Genetic algorithms, and Voting-Merging have been proposed. The vast majorit...

Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out emergent spurious structures that arise due to the various biases to which each participating algorithm is tuned. In this paper, we address the prob...

Consensus clustering methodologies combine a set of partitions on the clustering ensemble providing a consensus partition. One of the drawbacks of the standard combination algorithms is that all the partitions of the ensemble have the same weight on the aggregation process. By making a differentiation among the partitions the quality of the consensus could be improved. In this paper we propose ...

High dimensional data clustering arises naturally in a lot of domains, and have regularly presented a great deal with for usual data mining techniques. In this paper, presents an optimal perspective on the problem of Consensus Clustering in high-dimensional data. The proposed method called ―Fuzzy based and kernel mappings with Consensus Neighboring clustering (FKCNC)‖, which takes as key measur...