Neural networks for pattern recognition
Neural networks for pattern recognition
Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Partitioning-based clustering for Web document categorization
Decision Support Systems - Special issue on WITS '97
Concept decompositions for large sparse text data using clustering
Machine Learning
Machine Learning
Collective Intelligence and Braess' Paradox
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Collectives and Design Complex Systems
Collectives and Design Complex Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Scale-based clustering using the radial basis function network
IEEE Transactions on Neural Networks
Classifier ensembles: Select real-world applications
Information Fusion
Ensemble clustering with voting active clusters
Pattern Recognition Letters
Distributed clustering for group formation and task allocation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Multiagent based large data clustering scheme for data mining applications
AMT'10 Proceedings of the 6th international conference on Active media technology
Positional and confidence voting-based consensus functions for fuzzy cluster ensembles
Fuzzy Sets and Systems
A multi-agent based approach to clustering: harnessing the power of agents
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
A framework for Multi-Agent Based Clustering
Autonomous Agents and Multi-Agent Systems
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Numerous domains ranging from distributed data acquisition to knowledge reuse need to solve the cluster ensemble problem of combining multiple clusterings into a single unified clustering. Unfortunately current non-agent-based cluster combining methods do not work in a distributed environment, are not robust to corrupted clusterings and require centralized access to all original clusterings. Overcoming these issues will allow cluster ensembles to be used in fundamentally distributed and failure-prone domains such as data acquisition from satellite constellations, in addition to domains demanding confidentiality such as combining clusterings of user profiles. This paper proposes an efficient, distributed, agent-based clustering ensemble method that addresses these issues. In this approach each agent is assigned a small subset of the data and votes on which final cluster its data points should belong to. The final clustering is then evaluated by a global utility, computed in a distributed way. This clustering is also evaluated using an agent-specific utility that is shown to be easier for the agents to maximize. Results show that agents using the agent-specific utility can achieve better performance than traditional non-agent based methods and are effective even when up to 50% of the agents fail.