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Dheeraj Kumar

Associate Professor

Email : dheeraj.kumar[at]ece.iitr.ac.in
Phone: 01332-286159
Room No. : S 217
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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

DegreeSubjectUniversityYear Studied
Ph.D.Electrical & Electronic EngineeringThe University of Melbourne, Australia2017
B.Tech – M.Tech Dual degreeElectrical EngineeringIndian Institute of Technology (IIT) Kanpur2010
TitleCourse CodeCourseSemester
Digital Image ProcessingECN 316B. Tech 3rd YearSpring
Linear Algebra and Random ProcessesECN 511M.Tech 1st YearAutumn
Data Science for Smart CitiesCE 597Masters-PhD course at Purdue UniversityAutumn
TopicScholar NameStatus of PhDRegistration Year
Distributed data mining for IoT applicationsKartik Vishal DeshpandeO2019
Course NameSponsored ByDate Participated
SenZations summer school on applications of IoT and WSNSoCioTal (European Union – EU FP7 Project), IotLab and IERC13-Sep
Summer School on ?Computing for Socio-Economic development?Microsoft Research India10-Jun
Title of ProjectName of Student
Deep learning based Dark block extraction for automatic cluster tendency assessmentHarshal Mittal & Sai Laxman
Privacy preserving clustering tendency assessmentKartikey Pandey & Samar Singh Karnawat
Clustering tendency assessment for preference dataAnukarsh Khandelwal & Y Agarwal
  • 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.