Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
A method for decentralized clustering in large multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A novel ant clustering algorithm based on cellular automata
Web Intelligence and Agent Systems
Agent Mining: The Synergy of Agents and Data Mining
IEEE Intelligent Systems
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
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This paper presents an agent-based algorithm for discovering subspace clusters in high dimensional data. Each data object is represented by an agent, and the agents move from one local environment to another to find optimal clusters in subspaces. Heuristic rules and objective functions are defined to guide the movements of agents, so that similar agents(data objects) go to one group. The experimental results show that our proposed agent-based subspace clustering algorithm performs better than existing subspace clustering methods on both F1 measure and Entropy. The running time of our algorithm is scalable with the size and dimensionality of data. Furthermore, an application in stock market surveillance demonstrates its effectiveness in real world applications.