Semi-supervised visual clustering for spherical coordinates systems

ABSTRACT

In this paper we propose a method that combines the advanced data analysis of the automatic statistical methods and the flexibility and manual parameter tuning of interactive visual clustering. We present the Semi-Supervised Visual Clustering (SSVC) interface; its main contribution is the learning of the optimal projection distance metric for the star and spherical coordinate visualization systems. Beyond the conventional manual setting, it couples the visual clustering with the automatic setting where the projection distance metric is learned from the available set of user feedbacks in the form of either item similarities or direct item annotations. Moreover, SSVC interface allows for the hybrid setting where some parameters are manually set by the user while the remaining parameters are determined by the optimal distance algorithm.

REFERENCES

• 1: Sugato Basu , Mikhail Bilenko , Raymond J. Mooney, A probabilistic framework for semi-supervised clustering, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014062]
• 2: Christopher M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Inc., New York, NY, 1995
• 3: Keke Chen , Ling Liu, iVIBRATE: Interactive visualization-based framework for clustering large datasets, ACM Transactions on Information Systems (TOIS), v.24 n.2, p.245-294, April 2006 [doi>10.1145/1148020.1148024]
• 4: Boris Chidlovskii , Jérôme Fuselier , Loïc Lecerf, ALDAI: active learning documents annotation interface, Proceedings of the 2006 ACM symposium on Document engineering, October 10-13, 2006, Amsterdam, The Netherlands [doi>10.1145/1166160.1166208]
• 5: William S. Cleveland, Visualizing Data, Hobart Press, 1993
• 6: T. Hastie, R. Tibshirani, and J. H. Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer, 2001.
• 7: Eser Kandogan, Visualizing multi-dimensional clusters, trends, and outliers using star coordinates, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.107-116, August 26-29, 2001, San Francisco, California [doi>10.1145/502512.502530]
• 8: Yi Liu , Rong Jin , Anil K. Jain, BoostCluster: boosting clustering by pairwise constraints, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA [doi>10.1145/1281192.1281242]
• 9: Jinwook Seo , Ben Shneiderman, Interactively Exploring Hierarchical Clustering Results, Computer, v.35 n.7, p.80-86, July 2002 [doi>10.1109/MC.2002.1016905]
• 10: Wei Tang , Hui Xiong , Shi Zhong , Jie Wu, Enhancing semi-supervised clustering: a feature projection perspective, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA [doi>10.1145/1281192.1281268]
• 11: E. Xing, A. Ng, M. Jordan, and S. Russell. Distance metric learning, with application to clustering with side-information. Advances in NIPS, 15, 2003.
• 12: Lei Yu , Huan Liu, Efficient Feature Selection via Analysis of Relevance and Redundancy, The Journal of Machine Learning Research, 5, p.1205-1224, 12/1/2004

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering

General Terms:
Algorithms, Experimentation

Keywords:
semi-supervised clustering, star coordinates, visual clustering