Professor Christopher Leckie

  • Room: Level: 07 Room: 7.11
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Data Mining, Network Intrusion Detection, Artificial Intelligence for Telecommunications, Bioinformatics

Personal webpage

http://scholar.google.com/citations?user=wUsI0cAAAAAJ

Biography

Chris Leckie is a Professor in the Department of Computing and Information Systems at The University of Melbourne.

Research interests
- Artificial Intelligence (AI)
- telecommunications
- machine learning, fault diagnosis, distributed systems and design automation 

Prof Leckie has a strong interest in developing AI techniques for a variety of applications in telecommunications, such as network intrusion detection, network management, fault diagnosis and wireless sensor networks. He also has an interest in scalable data mining algorithms for tasks such as clustering and anomaly detection with applications in bioinformatics.

Recent publications

  1. Rathore P, Ghafoori Z, Bezdek J, Palaniswami M, Leckie C. Approximating Dunn's Cluster Validity Indices for Partitions of Big Data. IEEE Transactions on Cybernetics. Institute of Electrical and Electronics Engineers. 2018, Vol. PP. DOI: 10.1109/TCYB.2018.2806886
  2. Miao Y, Pan L, Rajasegarar S, Zhang J, Leckie C, Xiang Y. Distributed detection of zero-day network traffic flows. Communications in Computer and Information Science. Springer. 2018, Vol. 845. DOI: 10.1007/978-981-13-0292-3_11
  3. Ghafoori Z, Monazam Erfani S, Rajasegarar S, Bezdek J, Karunasekera S, Leckie C. Efficient Unsupervised Parameter Estimation for One-Class Support Vector Machines. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. Institute of Electrical and Electronics Engineers. 2018, Vol. 29, Issue 10. DOI: 10.1109/TNNLS.2017.2785792
  4. Kumar D, Wu H, Rajasegarar S, Leckie C, Krishnaswamy S, Palaniswami M. Fast and Scalable Big Data Trajectory Clustering for Understanding Urban Mobility. IEEE Transactions on Intelligent Transportation Systems. IEEE - Institute of Electrical and Electronic Engineers. 2018. DOI: 10.1109/TITS.2018.2854775
  5. Yang M, Rashidi L, Rajasegarar S, Leckie C. Graph stream mining based anomalous event analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2018, Vol. 11012 LNAI. DOI: 10.1007/978-3-319-97304-3_68
  6. Ganji M, Chan J, Stuckey P, Bailey J, Leckie C, Kotagiri R, Davidson I. Image constrained blockmodelling: A constraint programming approach. SIAM International Conference on Data Mining, SDM 2018. 2018.
  7. Chenaghlou M, Moshtaghi M, Leckie C, Salehi M. Online Clustering for Evolving Data Streams with Online Anomaly Detection. 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Springer Verlag. 2018, Vol. 10938. Editors: Phung D, Tseng VS, Webb GI, Ho B, Ganji M, Rashidi L. DOI: 10.1007/978-3-319-93037-4_40
  8. Lim K, Chan J, Leckie C, Karunasekera S. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. KNOWLEDGE AND INFORMATION SYSTEMS. Springer London. 2018, Vol. 54, Issue 2. DOI: 10.1007/s10115-017-1056-y
  9. Li L, Monazam Erfani S, Leckie C. A Pattern Tree based Method for Mining Conditional Contrast Patterns of Multi-Source Data. 17th IEEE International Conference on Data Mining (ICDMW). IEEE. 2017. Editors: Gottumukkala R, Ning X, Dong G, Raghavan V, Aluru S, Karypis G, Miele L, Wu X. DOI: 10.1109/ICDMW.2017.126
  10. Kumar D, Bezdek J, Rajasegarar S, Leckie C, Palaniswami M. A visual-numeric approach to clustering and anomaly detection for trajectory data. VISUAL COMPUTER. Springer. 2017, Vol. 33, Issue 3. DOI: 10.1007/s00371-015-1192-x
  11. Chenaghlou M, Moshtaghi M, Leckie C, Salehi M. An efficient method for anomaly detection in non-stationary data streams. GLOBECOM 2017 - 2017 IEEE GLOBAL COMMUNICATIONS CONFERENCE. Institute of Electrical and Electronics Engineers. 2017.
  12. Iredale T, Monazam Erfani S, Leckie C. An Efficient Visual Assessment of Cluster Tendency Tool for Large-scale Time Series Data Sets. 2017 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE). Institute of Electrical and Electronics Engineers. 2017. DOI: 10.1109/FUZZ-IEEE.2017.8015587
  13. Ristanoski G, Soni R, Rajasegarar S, Bailey J, Leckie C. Clustering aided support vector machines. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10358 LNAI. DOI: 10.1007/978-3-319-62416-7_23
  14. Liu L, Kan A, Leckie C, Hodgkin P. Comparative evaluation of performance measures for shading correction in time-lapse fluorescence microscopy. JOURNAL OF MICROSCOPY. Blackwell Science. 2017, Vol. 266, Issue 1. DOI: 10.1111/jmi.12512
  15. Moshtaghi M, Monazam Erfani S, Leckie C, Bezdek J. Exponentially Weighted Ellipsoidal Model for Anomaly Detection. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS. John Wiley & Sons. 2017, Vol. 32, Issue 9. DOI: 10.1002/int.21875

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile