Evidence Accumulation Clustering Based on the K-Means Algorithm
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Feature Weighting in k-Means Clustering
Machine Learning
Ensembles of Partitions via Data Resampling
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Scalable density-based distributed clustering
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective and Efficient Distributed Model-Based Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Data mining techniques such as clustering are usually applied to centralized data sets. At present, more and more data is generated and stored in local sites. The transmission of the entire local data set to server is often unacceptable because of performance considerations, privacy and security aspects, and bandwidth constraints. In this paper, we propose a distributed clustering model based on ensemble learning, which could analyze and mine distributed data sources to find global clustering patterns. A typical scenario of the distributed clustering is a 'two-stage' course, i.e. firstly doing clustering in local sites and then in global site. The local clustering results transmitted to server site form an ensemble and combining schemes of ensemble learning use the ensemble to generate global clustering results. In the model, generating global patterns from ensemble is mathematically converted to be a combinatorial optimization problem. As an implementation for the model, a novel distributed clustering algorithm called DK-means is presented. Experimental results show that DK-means achieves similar results to K-means which clusters centralized data set at a time and is scalable to data distribution varying in local sites, and show validity of the model.