Sridhar Ramaswamy
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Sridhar Ramaswamy is a person.
References
- DBLP Author Page: http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/r/Ramaswamy:Sridhar.html
2001
- (Ramswamy et al., 2001) ⇒ Sridhar Ramaswamy, Pablo Tamayo, Ryan Rifkin, Sayan Mukherjee, Chen-Hsiang Yeang, Michael Angelo, Christine Ladd, Michael Reich, Eva Latulippe, Jill P. Mesirov, Tomaso Poggio, William Gerald, Massimo Loda, Eric S. Lander, and Todd R. Golub. (2001). “Multiclass cancer diagnosis using tumor gene expression signatures.” In: Proceedings of the National Academy of Sciences of the United States of America (PNAS), 98(26) doi:10.1073/pnas.211566398
- (Ramswamy et al., 2001) ⇒ Sridhar Ramaswamy, Pablo Tamayo, Ryan Rifkin, Sayan Mukherjee, Chen-Hsiang Yeang, Michael Angelo, Christine Ladd, et al. (2001). “Multiclass cancer diagnosis using tumor gene expression signatures." Proceedings of the National Academy of Sciences 98, no. 26 (2001): 15149-15154.
2000
- (Ramaswamy et al., 2000) ⇒ Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim. (2000). “Efficient Algorithms for Mining Outliers from Large Data Sets.” In: SIGMOD Record, 29(20). doi:10.1145/335191.335437.
- ABSTRACT: In this paper, we propose a novel formulation for distance-based outliers that is based on the distance of a point from its kth nearest neighbor. We rank each point on the basis of its distance to its kth nearest neighbor and declare the top n points in this ranking to be outliers. In addition to developing relatively straightforward solutions to finding such outliers based on the classical nested-loop join and index join algorithms, we develop a highly efficient partition-based algorithm for mining outliers. This algorithm first partitions the input data set into disjoint subsets, and then prunes entire partitions as soon as it is determined that they cannot contain outliers. This results in substantial savings in computation. We present the results of an extensive experimental study on real-life and synthetic data sets. The results from a real-life NBA database highlight and reveal several expected and unexpected aspects of the database. The results from a study on synthetic data sets demonstrate that the partition-based algorithm scales well with respect to both data set size and data set dimensionality.