Abstract
Semi-supervised clustering models, that incorporate user provided constraints to yield meaningful clusters, have recently become a popular area of research. In this paper, we propose a cluster-level semi-supervision model for inter-active clustering. Prototype based clustering algorithms typically alternate between updating cluster descriptions and assignment of data items to clusters. In our model, the user provides semi-supervision directly for these two steps. Assignment feedback re-assigns data items among existing clusters, while cluster description feedback helps to position existing cluster centers more meaningfully. We argue that providing such supervision is more natural for exploratory data mining, where the user discovers and interprets clusters as the algorithm progresses, in comparison to the pair-wise instance level supervision model, particularly for high dimensional data such as document collection. We show how such feedback can be interpreted as constraints and incorporated within the kmeans clustering framework. Using experimental results on multiple real-world datasets, we show that this framework improves clustering performance significantly beyond traditional k-means. Interestingly, when given the same number of feedbacks from the user, the proposed framework significantly outperforms the pair-wise supervision model.
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References
Banerjee, A., Ghosh, J.: Scalable clustering algorithms with balancing constraints. Data Mining and Knowledge Discovery 13(3) (2006)
Bar-Hillel, A., Hertz, T., Shental, N., Weinshall, D.: Learning distance functions using equivalence relations. In: Proc. of ICML (2003)
Basu, S., Banjeree, A., Mooney, E.: Active semi-supervision for pairwise constrained clustering. In: Proc. of SDM (2004)
Basu, S., Davidson, I., Wagstaff, K.: Constrained clustering: Advances in algorithms, theory, and applications. Chapman and Hall/CRC Data Mining and Knowledge Discovery Series (2008)
Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: Proc. of ICML (2002)
Cohn, D., Caruana, R., McCallum, A.: Semi-supervised clustering with user feedback. Tech. rep., TR2003-1892, Cornell University (2003)
Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Machine Learning 15(2) (1994)
Cohn, D., Ghahramani, Z., Jordan, M.: Active learning with statistical models. Journal of Artificial Intelligence Research 4(1) (1996)
Davidson, I., Ravi, S.: Clustering with constraints: Feasibility issues and the k-means algorithm. In: Proc. of SDM (2005)
Davidson, I., Ravi, S.: Identifying and generating easy sets of constraints for clustering. In: Proc. of AAAI (2006)
Davidson, I., Ravi, S.: Intractability and clustering with constraints. In: Proc. of ICML (2007)
desJardins, M., MacGlashan, J., Ferraioli, J.: Interactive visual clustering. In: Proc. of IUI (2007)
Dhillon, I., Mallela, S., Modha, D.: Information-theoretic co-clustering. In: Proc. of SIGKDD (2003)
Gondek, D., Hofmann, T.: Non-redundant data clustering. In: Proc. of ICDM (2004)
Hofmann, T., Buhmann, J.: Active data clustering. In: Proc. of NIPS (1998)
Klein, D., Kamvar, S., Manning, C.: From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In: Proc. of ICML (2002)
Wagstaff, K., Cardie, C.: Clustering with instance-level constraints. In: Proc. of ICML (2000)
Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: Proc. of ICML (2001)
Xing, E., Ng, A., Jordan, M., Russell, S.: Distance metric learning, with application to clustering with side-information. In: Proc. of NIPS (2002)
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Dubey, A., Bhattacharya, I., Godbole, S. (2010). A Cluster-Level Semi-supervision Model for Interactive Clustering. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science(), vol 6321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15880-3_32
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DOI: https://doi.org/10.1007/978-3-642-15880-3_32
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