Abstract
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.
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Luo, C., Zhao, Y., Luo, D., Zhang, C., Cao, W. (2011). Agent-Based Subspace Clustering. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20847-8_31
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DOI: https://doi.org/10.1007/978-3-642-20847-8_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20846-1
Online ISBN: 978-3-642-20847-8
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