Algorithms for clustering data
Algorithms for clustering data
Parallel algorithms for hierarchical clustering
Parallel Computing
Yenta: a multi-agent, referral-based matchmaking system
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Large-Scale Parallel Data Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Cure: an efficient clustering algorithm for large databases
Information Systems
Matchmaking among minimal agents without a facilitator
Proceedings of the fifth international conference on Autonomous agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Dynamic Coalition Formation among Rational Agents
IEEE Intelligent Systems
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Task allocation via coalition formation among autonomous agents
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Coalition formation among bounded rational agents
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A Multi-Agent Approach for Peer-to-Peer Based Information Retrieval System
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
On Decentralised Clustering in self-monitoring networks
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A deployed multi-agent framework for distributed energy applications
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Boosting topic-based publish-subscribe systems with dynamic clustering
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Designing Peer-To-Peer Agent Auctions Using Object-Process Methodology
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Enhancing MAS cooperative search through coalition partitioning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Dynamic decentralized any-time hierarchical clustering
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
Multi-goal economic search using dynamic search structures
Autonomous Agents and Multi-Agent Systems
Multiagent based large data clustering scheme for data mining applications
AMT'10 Proceedings of the 6th international conference on Active media technology
Agent-based subspace clustering
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
On convergence of dynamic cluster formation in multi-agent networks
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Agent community extraction for 2d-robosoccer
RoboCup 2005
Towards adaptive clustering in self-monitoring multi-agent networks
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Group formation among peer-to-peer agents: learning group characteristics
AP2PC'03 Proceedings of the Second international conference on Agents and Peer-to-Peer Computing
Coverage density as a dominant property of large-scale sensor networks
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
Predicting cluster formation in decentralized sensor grids
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Design and evaluation of decentralized online clustering
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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This paper examines a method of clustering within a fully decentralized multi-agent system. Our goal is to group agents with similar objectives or data, as is done in traditional clustering. However, we add the additional constraint that agents must remain in place on a network, instead of first being collected into a centralized database. To do this we connect agents in a random network and have them search in a peer-to-peer fashion for other similar agents. We thus aim to tackle the basic clustering problem on an Internet scale and create a method by which agents themselves can be grouped, forming coalitions. In order to investigate the feasibility of a decentralized approach, this paper presents a number of simulation experiments involving agents representing two-dimensional points. A comparison between our method's clustering ability and that of the k-means clustering algorithm is presented. Generated data sets containing 2,500 to 160,000 points (agents) grouped in 25 to 1,600 clusters are examined. Results show that our decentralized agent method produces a better clustering than the centralized k-means algorithm, quickly placing 95% to 99% of points correctly. The the time required to find a clustering depends on the quality of solution required; a fairly good solution is quickly converged on, and then slowly improved. Overall, our experiments indicate that the time to find a particular quality of solution increases less than linearly with the number of agents.