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Dheeraj Kumar

Dheeraj Kumar

Assistant Professor

dheeraj.kumar@ece.iitr.ac.in
01332-286159
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Big Data mining, Clustering tendency assessment for high volume, high velocity, & high dimensional data, Distributed data mining for IoT applications, Smart city and Intelligent transportation, Data driven analysis of transportation problems, Hurricane evacuation modeling using social media data

Degree Subject University Year Studied
Ph.D. Electrical & Electronic Engineering The University of Melbourne, Australia 2017
B.Tech - M.Tech Dual degree Electrical Engineering Indian Institute of Technology (IIT) Kanpur 2010
Title Course Code Course Semester
Digital Image Processing ECN 316 B. Tech 3rd Year Spring
Linear Algebra and Random Processes ECN 511 M.Tech 1st Year Autumn
Data Science for Smart Cities CE 597 Masters-PhD course at Purdue University Autumn
Topic Scholar Name Status of PhD Registration Year
Distributed data mining for IoT applications Kartik Vishal Deshpande O 2019
Course Name Sponsored By Date Participated
SenZations summer school on applications of IoT and WSN SoCioTal (European Union - EU FP7 Project), IotLab and IERC 13-Sep
Summer School on ?Computing for Socio-Economic development? Microsoft Research India 10-Jun
Title of Project Name of Student
Deep learning based Dark block extraction for automatic cluster tendency assessment Harshal Mittal & Sai Laxman
Privacy preserving clustering tendency assessment Kartikey Pandey & Samar Singh Karnawat
Clustering tendency assessment for preference data Anukarsh Khandelwal & Y Agarwal
Institute Visited Purpose of Visit Visit Date
Institute for Infocomm Research (I2R) - A Star Foreign Student Attachment (Internship) Nov-15

S. Mahallati, J.C. Bezdek, D. Kumar, M.R. Popovic, and T.A. Valiante, “Interpreting Cluster Structure in Waveform Data with Visual Assessment and Dunn's Index.” Frontiers in Computational Intelligence - Springer, pp. 73–101, 2017.

  1. P. Rathore, D. Kumar, S. Rajasegarar, M. S. Palaniswami, and J. C. Bezdek "Visual Structural Assessment and Anomaly Detection for High-Velocity Data Streams," in IEEE Transactions on Cybernetics (T-CYB), accepted.
  2. X. Qian, D. Kumar, W. Zhang, and S. V. Ukkusuri, "Understanding the operational dynamics of Mobility Service Providers: A case of Uber," in ACM Transactions on Spatial Algorithms and Systems (TSAS), vol. 6, no. 2, pp. 12:1-12:20, Feb. 2020.
  3. D. Kumar and J. C. Bezdek "Visual approaches for exploratory data analysis: A survey of the VAT family of algorithms," in IEEE Systems, Man, and Cybernetics Magazine (SMC-MAG), accepted.
  4. M. Palaniswami, A. S. Rao, D. Kumar, P. Rathore, and S. Rajasegarar, "Role of Visual Assessment of Clusters for Big Data Analysis from Real-world Internet of Things," in IEEE Systems, Man, and Cybernetics Magazine (SMC-MAG), accepted.
  5. P. Rathore, D. Kumar, S. Rajasegarar, M. S. Palaniswami, and J. C. Bezdek " A Scalable Framework for Trajectory Prediction," in IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2019, Accepted.
  6. D. Kumar, Z. Ghafoori, J. C. Bezdek, C. Leckie, K. Ramamohanarao, and M., Palaniswami, " Dealing with Inliers in Feature Vector Data," in International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS), 2018, Accepted.
  7. D. Kumar, H. Wu, S. Rajasegarar, C. Leckie, S. Krishnaswamy and M. S. Palaniswami, " Fast and Scalable Big Data Trajectory Clustering for Understanding Urban Mobility," in IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2018, Accepted.
  8. P. Rathore, D. Kumar, J. C. Bezdek, S. Rajasegarar and M. S. Palaniswami, "A Rapid Hybrid Clustering Algorithm for Large Volumes of High Dimensional Data," in IEEE Transactions on Knowledge & Data Engineering (TKDE), 2018, Accepted.
  9. P. Rathore, D. Kumar, S. Rajasegarar, and M. Palaniswami, “Maximum Entropy based Auto Drift Correction using High and Low Precision Sensors.” ACM Transactions on Sensor Networks (TOSN), vol. 13, no. 3, Apr. 2017.
  10. D. Kumar, J. Bezdek, S. Rajasegarar, M. Palaniswami, C. Leckie, J. Chan, and J. Gubbi, “Adaptive Cluster Tendency Visualization and Anomaly Detection for Streaming Data.” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 11, no. 2, pp. 24:1-24:40, Dec 2016.
  11. D. Kumar, J. Bezdek, S. Rajasegarar, C. Leckie, and M. Palaniswami, “A Visual-Numeric Approach to Clustering and Anomaly Detection for Trajectory Data.” The Visual Computer - Springer, vol. 33, no. 3, pp. 265-281, 2017.
  12. D. Kumar, J. Bezdek, M. Palaniswami, S. Rajasegarar, C. Leckie, and T. Havens, “A Hybrid Approach to Clustering in Big Data.” IEEE Transactions on Cybernetics, vol. 46, no. 10, pp. 2372-2385, Oct. 2016.
  13. D. Kumar, S. Rajasegarar, and M. Palaniswami, “Geospatial estimation based auto drift correction in wireless sensor networks,” ACM Transactions on Sensor Networks (TOSN), vol. 11, no. 3, pp. 50:1–50:39, Apr. 2015.
  14. D. Kumar, P. Vimal and Rajesh M. Hegde, “On the Soft Fusion of Probability Mass Functions for Multimodal Speech Processing,” EURASIP Journal on Advances in Signal Processing, vol. 2011, Article ID 294010, 2011.
Total Citations
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