Multi-dimensional clustering for data base organizations

Multi-dimensional clustering for data base organizations

Multi-view Subspace Clustering for High-dimensional Data

The data today is towards more observations and very high dimensions. Large high-dimensional data are usually sparse and contain many classes/clusters. For example, large text data in the vector space model often contains many classes of documents represented in thousands of terms. It has become a rule rather than the exception that clusters in high-dimensional data occur in subspaces of data, ...

Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

Toward Multi-Diversified Ensemble Clustering of High-Dimensional Data

The emergence of high-dimensional data in various areas has brought new challenges to the ensemble clustering research. To deal with the curse of dimensionality, considerable efforts in ensemble clustering have been made by incorporating various subspace-based techniques. Besides the emphasis on subspaces, rather limited attention has been paid to the potential diversity in similarity/dissimila...

Distributed data clustering in multi-dimensional peer-to-peer networks

Several algorithms have been recently developed for distributed data clustering, which are applied when data cannot be concentrated on a single machine, for instance because of privacy reasons or due to network bandwidth limitations, or because of the huge amount of distributed data. Deployed and research Peer-to-Peer systems have proven to be able to manage very large databases made up by thou...