Deepak P
Queen's University Belfast
90 Papers
381 Citations
Deepak P is an academic researcher from Queen's University Belfast. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 14, co-authored 86 publications. Previous affiliations of Deepak P include Queen's University & Association for Computing Machinery.
Chat about Author
Papers
Indexing and matching trajectories under inconsistent sampling rates
Sayan Ranu,Deepak P,Aditya Telang,Prasad M. Deshpande,Sriram Raghavan +4 more
- 13 Apr 2015
TL;DR: A robust distance function called Edit Distance with Projections (EDwP) to match trajectories under inconsistent and variable sampling rates through dynamic interpolation is formulated, and an index structure called TrajTree is designed to enable efficient trajectory retrievals using EDwP.
Unsupervised Fake News Detection: A Graph-based Approach
Siva Charan Reddy Gangireddy,Deepak P,Cheng Long,Tanmoy Chakraborty +3 more
- 13 Jul 2020
TL;DR: GTUT, a graph-based approach for unsupervised fake news detection, is developed which operates in three phases and establishes the improved effectiveness of the method over state-of-the-art techniques for the task.
CAESAR: A Context-Aware, Social Recommender System for Low-End Mobile Devices
Lakshmish Ramaswamy,Deepak P,Ramana V. Polavarapu,Kutila Gunasekera,Dinesh Garg,Karthik Visweswariah,Shivkumar Kalyanaraman +6 more
- 18 May 2009
TL;DR: A social network-based recommender system that has been explicitly designed to work even with devices that just support phone calls and SMS, and which outlines the challenges in building such a system, and outline approaches to deal with such challenges.
65
•Posted Content
Fairness in Clustering with Multiple Sensitive Attributes
TL;DR: The experimental evaluation illustrates that the clusters generated by FairKM fare significantly better on both clustering quality and fair representation of sensitive attribute groups compared to the clusters from a state-of-the-art baseline fair clustering method.
35
Efficient reverse skyline retrieval with arbitrary non-metric similarity measures
Prasad M. Deshpande,Deepak P +1 more
- 21 Mar 2011
TL;DR: This paper considers Reverse Skyline query processing where the distance between attribute values are not necessarily metric, and proposes a method of using group-level reasoning and early pruning to micro-optimize processing by reducing attribute level comparisons.