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- Ramesh C. Agarwal, Charu C. Aggarwal, V. V. V. Prasad
- J. Parallel Distrib. Comput.
- 2001

In this paper we propose algorithms for generation of frequent item sets by successive construction of the nodes of a lexicographic tree of item sets. We discuss different strategies in generation… (More)

ABSTRACT In this paper we present an algorithm for mining long patterns in databases. The algorithm nds large itemsets by using depth rst search on a lexicographic tree of itemsets. The focus of this… (More)

- Ramesh C. Agarwal, Mahesh V. Joshi
- SDM
- 2001

Learning classifier models is an important problem in data mining. Observations from the real world are often recorded as a set of records, each characterized by multiple attributes. Associated with… (More)

- Mahesh V. Joshi, V. V. Ajith Kumar, Ramesh C. Agarwal
- Proceedings IEEE International Conference on…
- 2001

Classification of rare events has many important data mining applications. Boosting is a promising meta-technique that improves the classification performance of any weak classifier. So far, no… (More)

- Mahesh V. Joshi, Ramesh C. Agarwal, V. V. Ajith Kumar
- SIGMOD Conference
- 2001

Learning models to classify rarely occurring target classes is an important problem with applications in network intrusion detection, fraud detection, or deviation detection in general. In this… (More)

- Ramesh C. Agarwal, Susanne M. Balle, Fred G. Gustavson, Mahesh V. Joshi, Prasad V. Palkar
- IBM Journal of Research and Development
- 1995

A three-dimensional (3D) matrix multiplication algorithm for massively parallel processing systems is presented. The P processors are configured as a "virtual" processing cube with dimensions pl, p2,… (More)

- Ramesh C. Agarwal, Fred G. Gustavson, Mohammad Zubair
- IBM Journal of Research and Development
- 1994

We describe the algorithms and architecture approach to produce high-performance codes for numerically intensive computations. In this approach, for a given computation, we design algorithms so that… (More)

Boosting is a strong ensemble-based learning algorithm with the promise of iteratively improving the classification accuracy using any base learner, as long as it satisfies the condition of yielding… (More)

- Ramesh C. Agarwal, Fred G. Gustavson, Mohammad Zubair
- Proceedings Supercomputing '92
- 1992

The authors propose a feature-extraction-based algorithm (FEBA) for sparse matrix-vector multiplication. The key idea of FEBA is to exploit any regular structure present in the sparse matrix by… (More)

Database systems are not well-tuned to take advantage of modern superscalar processor architectures. In particular, the clocks per instruction (CPI) for rather simple database queries are quite poor… (More)