Sonal Kumari
Birla Institute of Technology and Science
13 Papers
58 Citations
Sonal Kumari is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Cluster analysis & Parallel algorithm. The author has an hindex of 5, co-authored 13 publications. Previous affiliations of Sonal Kumari include Samsung.
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Papers
Human Action Recognition Using DFT
Sonal Kumari,Suman K. Mitra +1 more
- 15 Dec 2011
TL;DR: The proposed novel action recognition algorithm uses discrete Fourier transform (DFT) of the small image block to deal with the noise caused because of illumination effects, blurring, false contour etc.
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μDBSCAN: An Exact Scalable DBSCAN Algorithm for Big Data Exploiting Spatial Locality
Aditya Sarma,Poonam Goyal,Sonal Kumari,Anand Wani,Jagat Sesh Challa,Saiyedul Islam,Navneet Goyal +6 more
- 01 Sep 2019
TL;DR: This work proposes a micro-cluster based DBSCAN algorithm, μDBSCAN, which identifies core-points even without performing neighbourhood queries and becomes instrumental in reducing the run-time of the algorithm, which significantly reduces the computation time per neighbourhood query while producing exact DBS CAN clusters.
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Exact, Fast and Scalable Parallel DBSCAN for Commodity Platforms
Sonal Kumari,Poonam Goyal,Ankit Sood,Dhruv Kumar,Sundar Balasubramaniam,Navneet Goyal +5 more
- 05 Jan 2017
TL;DR: A grid-based DBSCAN algorithm, GridDBSCAN, is presented, which is significantly faster than the state-of-the-art sequential DBS CAN and its parallel implementations, and also proposes scalable parallel implementations of GridD BSCAN to leverage a multicore commodity cluster.
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A Fast, Scalable SLINK Algorithm for Commodity Cluster Computing Exploiting Spatial Locality
Poonam Goyal,Sonal Kumari,Sumit Sharma,Dhruv Kumar,Vivek Kishore,Sundar Balasubramaniam,Navneet Goyal +6 more
- 01 Dec 2016
TL;DR: This paper presents a novel optimization of SLINK algorithm, GridSLINK, which is an order of magnitude faster than the existing state-of-the-art implementation and is benchmarked against the best existing parallel algorithm in literature and found to outperform the latter.
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Parallelizing OPTICS for Commodity Clusters
Poonam Goyal,Sonal Kumari,Dhruv Kumar,Sundar Balasubramaniam,Navneet Goyal,Saiyedul Islam,Jagat Sesh Challa +6 more
- 04 Jan 2015
TL;DR: The proposed DOPTICS algorithm is a parallelized version of a popular density based cluster-ordering algorithm OPTICS that is found to scale well with increasing number of processing elements and to achieve high parallelism.
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