A domain specific language for clustering

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Abstract

Clustering of large volumes of data is a complex problem which requires use of sophisticated algorithms as well as High Performance Computing hardware like a cluster of computers. It is highly desirable that data mining experts have a solution which on one hand provides a simple interface for ex-pressing their algorithms in terms of domain specific idioms and on the other hand automatically generates parallel code that can run on a cluster of multicore nodes. The proposed Domain Specific Language (DSL) along with its parallelizing compiler attempts to provide a solution. In this paper, we give the design of the DSL, called DWARF. Various language constructs have been described along with the rationale behind their inclusion in the language. A qualitative comparison of abstraction provided by DWARF is compared with MapReduce, Spark, and other MPI-based implementations to establish the usefulness of the proposed clustering DSL.

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Islam, S., Balasubramaniam, S., Goyal, P., Sati, M., & Goyal, N. (2017). A domain specific language for clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10109 LNCS, pp. 231–234). Springer Verlag. https://doi.org/10.1007/978-3-319-50472-8_19

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