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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 itemsets by successive construction of the nodes of a lexicographic tree of itemsets. We discuss di erent strategies in generation and traversal of the lexicographic tree such as breadthrst search, depthrst search or a combination of the two. These techniques provide di erent trade-o s in terms… (More)

In this paper we presen t 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 paper is to dev elop CPU-e cient algorithms for nding frequent itemsets in the cases when the database contains patterns which are v ery wide.We refer to this… (More)

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

- Mahesh V. Joshi, Vipin Kumar, Ramesh C. Agarwal
- ICDM
- 2001

Classification of rare events has many important data mining applications. Boosting is a promising metatechnique that improves the classification performance of any weak classifier. So far, no systematic study has been conducted to evaluate how boosting performs for the task of mining rare classes. In this paper, we evaluate three existing categories of… (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, and p3 proportional to the matrices' dimensions-M, N, and K. Each processor performs a single local matrix multiplication of size Mlp, x Nlp, x Wp,. Before the… (More)

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 each record is a categorical attribute called class. Given a training set of records with known class labels, the problem is to learn a model for the class in… (More)

- Mahesh V. Joshi, Ramesh C. Agarwal, Vipin 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 paper, we analyze our previously proposed two-phase rule induction method in the context of learning complete and precise signatures of rare classes. The key feature… (More)

- Ramesh C. Agarwal
- SIGMOD Conference
- 1996

The compare and branch sequences required in a traditional sort algorithm can not efficiently exploit multiple execution units present in currently available high performance RISC processors. This is because of the long latency of the compare instructions and the sequential algorithm used in sorting. With the increased level of integration on a chip, this… (More)

In this paper we propose a feature extraction based algorithm (FEBA) for the sparse matriz-vector multiplication. The key idea of FEBA is to ezploit any regular structure present in the sparse matriz by extracting it and processing it separately. The order in which these structures are eztracted is determined by the relative eficiency with which they can be… (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 they perform optimally when run on a target machine-in this case, the new POWERS'" machines from the RSl6000 family of RISC processors. The algorithmic features… (More)