High-dimensional clustering; Text clustering; Unsupervised learning on document datasets
At a high-level the problem of document clustering is defined as follows. Given a set S of n documents, we would like to partition them into a pre-determined number of k subsets S1, S2, …, Sk, such that the documents assigned to each subset are more similar to each other than the documents assigned to different subsets. Document clustering is an essential part of text mining and has many applications in information retrieval and knowledge management. Document clustering faces two big challenges: the dimensionality of the feature space tends to be high (i.e., a document collection often consists of thousands or tens of thousands unique words); the size of a document collection tends to be large.
Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms as well as in facilitating...
- 1.Boley D. Principal direction divisive partitioning. Data Mining Knowl Discov. 1998; 2(4): 325–44.Google Scholar
- 2.Cutting DR, Pedersen JO, Karger DR, Tukey JW. Scatter/gather: a cluster-based approach to browsing large document collections. In: Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 1992. p. 318–29.Google Scholar
- 4.Dhillon IS. Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2001. p. 269–74.Google Scholar
- 5.Ding C., He X., Zha H., Gu M., and Simon H. 1Spectral min-max cut for graph partitioning and data clustering. Technical Report TR-2001-XX, Lawrence Berkeley National Laboratory, University of California, Berkeley, 2001.Google Scholar
- 9.Karypis G. Cluto: a clustering toolkit. Technical Report 02-017, Department of Computer Science, University of Minnesota, 2002.Google Scholar
- 11.MacQueen J. Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Symposium on Mathematical Statistics and Probablity. 1967; p. 281–97.Google Scholar
- 12.Salton G. Automatic text processing: the transformation, analysis, and retrieval of information by computer. Reading: Addison-Wesley; 1989.Google Scholar
- 15.Zha H, He X, Ding C, Simon H, Gu M. Bipartite graph partitioning and data clustering. In: Proceedings of the International Conference on Information and Knowledge Management; 2001.Google Scholar