Datasets for unsupervised clustering can be large and sparse, with significant portion of missing values. We present here a scalable version of a robust clustering method with the available data strategy. Moreprecisely, a general algorithm is described and the accuracy and scalability of a distributed implementation of the algorithm is tested. The obtained results allow us to conclude the viability of the proposed approach.peerReviewe
Subspace clustering refers to the task of finding a multi-subspace representation that best fits a c...
In many situations where the interest lies in identifying clusters one might expect that not all ava...
Clustering very large datasets while preserving cluster quality remains a challenging data-mining ta...
Fast and eective unsupervised clustering is a fundamental tool in unsupervised learning. Here is a n...
One of the most widely used techniques for data clustering is agglomerative clustering. Such algorit...
We introduce a robust k-means-based clustering method for high-dimensional data where not only outli...
One of the most widely used techniques for data clustering is agglomerative clustering. Such al-gori...
This thesis focuses on developing a few robust learning algorithms, which aim to overcome the major ...
Searching a dataset for the ‘‘natural grouping / clustering’’ is an important explanatory technique ...
This paper focuses on scalability and robustness of spectral clustering for extremely large-scale da...
How do we find a natural clustering of a real world point set, which contains an unknown number of c...
Clustering algorithms are an important tool for data mining and data analysis purposes. Clustering a...
Clustering is defined as the process of grouping a set of objects in a way that objects in the same ...
How do we find a natural clustering of a real world point set, which contains an unknown number of c...
Finding clusters in data is a challenging problem especially when the clusters are being of widely v...
Subspace clustering refers to the task of finding a multi-subspace representation that best fits a c...
In many situations where the interest lies in identifying clusters one might expect that not all ava...
Clustering very large datasets while preserving cluster quality remains a challenging data-mining ta...
Fast and eective unsupervised clustering is a fundamental tool in unsupervised learning. Here is a n...
One of the most widely used techniques for data clustering is agglomerative clustering. Such algorit...
We introduce a robust k-means-based clustering method for high-dimensional data where not only outli...
One of the most widely used techniques for data clustering is agglomerative clustering. Such al-gori...
This thesis focuses on developing a few robust learning algorithms, which aim to overcome the major ...
Searching a dataset for the ‘‘natural grouping / clustering’’ is an important explanatory technique ...
This paper focuses on scalability and robustness of spectral clustering for extremely large-scale da...
How do we find a natural clustering of a real world point set, which contains an unknown number of c...
Clustering algorithms are an important tool for data mining and data analysis purposes. Clustering a...
Clustering is defined as the process of grouping a set of objects in a way that objects in the same ...
How do we find a natural clustering of a real world point set, which contains an unknown number of c...
Finding clusters in data is a challenging problem especially when the clusters are being of widely v...
Subspace clustering refers to the task of finding a multi-subspace representation that best fits a c...
In many situations where the interest lies in identifying clusters one might expect that not all ava...
Clustering very large datasets while preserving cluster quality remains a challenging data-mining ta...