Publication date
January 2005
DOIAbstract
This thesis focuses on developing a few robust learning algorithms, which aim to overcome the major drawbacks of traditional clustering methods, for data clustering and its extensions.DOCTOR OF PHILOSOPHY (EEE
This thesis focuses on developing a few robust learning algorithms, which aim to overcome the major drawbacks of traditional clustering methods, for data clustering and its extensions.DOCTOR OF PHILOSOPHY (EEE
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised cl...
How do we find a natural clustering of a real world point set, which contains an unknown number of c...
This thesis presents the design and implementation of a knowledge-oriented clustering algorithm that...
As mentioned in the title, the framework of this doctoral dissertation encompasses two different sub...
The article analyzes clustering problems that arise in forecasting tasks when clustering short time ...
Abstract—Clustering methods need to be robust if they are to be useful in practice. In this paper, w...
The purpose of this thesis is to present our research works on some of the fundamental issues encoun...
A growing number of data-based applications are used for decision-making that have far-reaching cons...
The paper introduces a robust clustering algorithm that can automatically determine the unknown clus...
Clustering remains a vibrant area of research in statistics. Although there are many books on this t...
A unified theory is presented to assess the robustness of general clustering methods (GCM), i.e., me...
Research on the problem of clustering tends to be fragmented across the pattern recognition, databas...
<p>One of the most widely used techniques for data clustering is agglomerative clustering. Such algo...
How do we find a natural clustering of a real world point set, which contains an unknown number of c...
Datasets for unsupervised clustering can be large and sparse, with significant portion of missing va...
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised cl...
How do we find a natural clustering of a real world point set, which contains an unknown number of c...
This thesis presents the design and implementation of a knowledge-oriented clustering algorithm that...
As mentioned in the title, the framework of this doctoral dissertation encompasses two different sub...
The article analyzes clustering problems that arise in forecasting tasks when clustering short time ...
Abstract—Clustering methods need to be robust if they are to be useful in practice. In this paper, w...
The purpose of this thesis is to present our research works on some of the fundamental issues encoun...
A growing number of data-based applications are used for decision-making that have far-reaching cons...
The paper introduces a robust clustering algorithm that can automatically determine the unknown clus...
Clustering remains a vibrant area of research in statistics. Although there are many books on this t...
A unified theory is presented to assess the robustness of general clustering methods (GCM), i.e., me...
Research on the problem of clustering tends to be fragmented across the pattern recognition, databas...
<p>One of the most widely used techniques for data clustering is agglomerative clustering. Such algo...
How do we find a natural clustering of a real world point set, which contains an unknown number of c...
Datasets for unsupervised clustering can be large and sparse, with significant portion of missing va...
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised cl...
How do we find a natural clustering of a real world point set, which contains an unknown number of c...
This thesis presents the design and implementation of a knowledge-oriented clustering algorithm that...
Add to ORKG