poster

GPU-based out-of-core MDL clustering algorithm

Authors:

Piyush Madan, Jaismeen Kaur,

Sheetal Bhavsar,

Dhaval Patel,

Manoj MishraAuthors Info & Claims

CODS '15: Proceedings of the 2nd ACM IKDD Conference on Data Sciences

Pages 124 - 125

https://doi.org/10.1145/2732587.2732610

Published: 18 March 2015 Publication History

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Abstract

The time complexity of Minimum Description Length based greedy agglomerative clustering algorithm is poor for a large data set. In this paper, we propose three different versions of GPU-based parallel algorithms, namely, time-efficient, memory-efficient and out-of-core algorithms to improve the running time of algorithm. We conduct extensive set of experiments which prove that the parallel out-of-core version that we propose in this paper, is the most useful version for practical purposes.

References

[1]

D. Patel, W. Hsu, L. Li., "LinkNet: Capturing Temporal Dependencies among Spatial Region" Springer, Distributed and Parallel Databases, 2014.

Google Scholar

[2]

S. Sarawagi, A. Kirpal, "Efficient set joins on similarity predicates," in ACM SIGMOD international conference on Management of data, New York, NY, USA, 2004.

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Index Terms

GPU-based out-of-core MDL clustering algorithm
1. Information systems
  1. Information systems applications
    1. Data mining

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Information

Published In

CODS '15: Proceedings of the 2nd ACM IKDD Conference on Data Sciences

March 2015

150 pages

ISBN:9781450334365

DOI:10.1145/2732587

General Chairs:
Manish Gupta
Xerox Research Center, India
,
Y. Narahari
Indian Institute of Science, Bangalore
,
Program Chairs:
V. S. Subrahmanian
University of Maryland, College Park
,
Indrajit Bhattacharya
IBM Research, India

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2015

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CODS '15

CODS '15: 2nd IKDD Conference on Data Sciences

March 18 - 21, 2015

Bangalore, India

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Overall Acceptance Rate 197 of 680 submissions, 29%

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