BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Distributed clustering using collective principal component analysis
Knowledge and Information Systems
Incremental maintenance of multi-source views
ADC '01 Proceedings of the 12th Australasian database conference
Techniques of Cluster Algorithms in Data Mining
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Clustering Large Datasets in Arbitrary Metric Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Grid-Clustering: An Efficient Hierarchical Clustering Method for Very Large Data Sets
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Issues of agent-based distributed data mining
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A privacy-sensitive approach to distributed clustering
Pattern Recognition Letters - Special issue: Advances in pattern recognition
Visualizing Global Manifold Based on Distributed Local Data Abstractions
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Privacy-preserving agent-based distributed data clustering
Web Intelligence and Agent Systems
Privacy preserving clustering on horizontally partitioned data
Data & Knowledge Engineering
Merging distributed database summaries
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Selectivity estimation in spatial networks
Proceedings of the 2008 ACM symposium on Applied computing
Privacy Preserving Data Mining Research: Current Status and Key Issues
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
A scalable framework for cluster ensembles
Pattern Recognition
Node and edge selectivity estimation for range queries in spatial networks
Information Systems
Stream Clustering Based on Kernel Density Estimation
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
Efficient privacy preserving distributed clustering based on secret sharing
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Distributed data clustering in multi-dimensional peer-to-peer networks
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Approximate pairwise clustering for large data sets via sampling plus extension
Pattern Recognition
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
L2GClust: local-to-global clustering of stream sources
Proceedings of the 2011 ACM Symposium on Applied Computing
International Journal of Autonomous and Adaptive Communications Systems
On robust and effective k-anonymity in large databases
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
Clustering distributed data streams in peer-to-peer environments
Information Sciences: an International Journal
ACM Transactions on Knowledge Discovery from Data (TKDD)
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Peer-to-peer data mining classifiers for decentralized detection of network attacks
ADC '13 Proceedings of the Twenty-Fourth Australasian Database Conference - Volume 137
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Huge amounts of data are stored in autonomous, geographically distributed sources. The discovery of previously unknown, implicit and valuable knowledge is a key aspect of the exploitation of such sources. In recent years several approaches to knowledge discovery and data mining, and in particular to clustering, have been developed, but only a few of them are designed for distributed data sources. We propose a novel distributed clustering algorithm based on non-parametric kernel density estimation, which takes into account the issues of privacy and communication costs that arise in a distributed environment.