Professor Sanjay Chawla

PhD (University of Tennessee)
Professor of Pattern and Data Mining
School of Information Technologies

J12 - The School of Information Technologies
The University of Sydney

Telephone +61 2 9351 3423
Fax +61 2 9351 3838

Website School of Information Technologies

Pattern and Data Mining

Biographical details

Sanjay Chawla is Professor of Pattern and Data Mining in the School of Information Technologies, University of Sydney. He served as the Head of School during 2008-11.

His research work has appeared in leading data mining journals and conferences including ACM TKDD, Machine Learning, IEEE TKDE, DMKD, ACM SIGKDD, IEEE ICDM, SDM, and PAKDD.

He is an associate editor for IEEE TKDE and serves on the editorial board of Data Mining and Knowledge Discovery. He served as a Program Co-Chair of PAKDD 2012.

He received his PhD in 1995 from the University of Tennessee, Knoxville, USA under Professor Suzanne Lenhart.

Research interests

Computational methods and algorithms can be applied in almost any domain. Professor Sanjay Chawla's research in the fields of data mining, machine learning and data management has found its way into areas as diverse as health analytics, road traffic management, cybersecurity and internet advertising.

"I design computational methods to extract meaningful patterns from massive data sets.

"Sometimes I design a new method and then look for a domain where it can be applied. Other times I start with a specific problem and design a customised algorithm to address it.

"Examples of areas to which my work has been applied include health analytics, road traffic monitoring, cybersecurity, bioinformatics, computational advertising and medical informatics.

"My research into outlier detection has been applied to better our understanding of road traffic patterns and health insurance claims.

"I really enjoy solving problems, and I particularly like collaborating with colleagues around the world to design new computational methods and algorithms to jointly solve data-related problems of common interest - whether abstract or applied.

"Over time I hope that, along with others researchers in my community, I will be able to develop a standardised set of computational techniques that can be applied across many domains.

"I have been working in the field of data-mining for nearly 15 years, and joined the University of Sydney in 2002. Sydney attracts the best students, and it is inspiring to teach and work with them."

Teaching and supervision

COMP5318 - Knowledge Discovery and Data Mining

COMP5338 - Advanced Data Models

INFO3404 - Database Systems 2

INFO3504 - Database Systems 2 (Adv)

INFO5010 - IT Advanced Topic A

In the media

Selected grants

2014

  • Interim Project Agreement - The Sleep Database. CRC for Alertness Safety and Productivity; Chawla S; Alertness CRC Ltd/Research Agreement.
  • Multi-modal filtering for nonlinear dynamical systems; Ramos F, Chawla S, Fox D; DVC International/IPDF Grant.

2013

  • Hardware-Based Accelerators for Real-Time Machine Learning; Leong P, Jin C, Davis R, Chawla S, Chapman M; Australian Research Council (ARC)/Linkage Projects (LP).
  • Sydney Neuroscience Network; Balleine B, Absalom N, Braddon-Mitchell D, Callaghan S, Chawla S, Christie M, Collins M, Einfeld S, Grieve S, Haber P, Hanrahan J, Harris A, Hickie I, Kassiou M, Kril J, Meikle S, Morris R, Ramos F, Rong Y, Sue C; DVC Research/Research Network Scheme (SyReNS).
  • Probabilistic graphical models for detecting outbreaks; Ramos F, Chawla S; Australian Research Council (ARC)/Discovery Projects (DP).

2010

  • Forbidden Knowledge: Ethical and Technical Constraints on Knowledge Discovery; Chawla S, Chopra S; DVC Research/International Visiting Research Fellowship (IVRF).

2009

  • Data Mining for Insurance Surveillance; Chawla S; Capital Markets CRC/Research Support.

2008

  • Choice and Classification in either complex or adversarial environments; Agastya M, Chawla S; Australian Research Council (ARC)/Discovery Projects (DP).

2007

  • Cross market pattern mining for private information trades; Chawla S; CRC for Technology Enabled Capital Markets/Research Grant.

2006

  • CRC Capital Markets / #3534 / Project : Corporate governance technologies - audit explorer / Proj; Chawla S.

2005

  • New Directions in Mining Complex Spatial Relationships in Large Scientific Databases; Chawla S; Australian Research Council (ARC)/Discovery Projects (DP).

2003

  • Determining outlier patterns in health data; Hale R, Chawla S; Capital Markets CRC/Research Grants.
  • Extraction and Management of Spatially indexed time series; Chawla S; DVC Research/Research and Development Scheme: Newly Appointed Staff (NAS).

2002

  • Project IE-01 Pelican Defining the Context; Tobias J, Chawla S, Kay J, Kummerfeld R; Smart Internet Technology Cooperative Research Centre/Cooperative Research Centres.

Selected publications

Download citations: PDF RTF Endnote

Books

  • Shekhar, S., Chawla, S. (2003). Spatial Databases: A Tour. USA: Prentice Hall.

Edited Books

  • Chawla, S., Washio, T., Minato, S., Tsumoto, S., Onoda, T., Yamada, S., Inokuchi, A. (2009). New Frontiers in Applied Data Mining: PAKDD 2008 International Workshops - LNCS Volume 5433. Germany: Springer.

Book Chapters

  • Wu, E., Liu, W., Chawla, S. (2010). Spatio-temporal Outlier Detection in Precipitation Data. In M M Gaber, R R Vatsavai, O A Omitaomu, J Gama, N V Chawla & A R Ganguly (Eds.), Knowledge Discovery from Sensor Data - Revised Selected Papers (LNCS 5840), (pp. 115-133). Germany: Springer.

Journals

  • Babbar, S., Surian, D., Chawla, S. (2013). A Causal Approach for Mining Interesting Anomalies. Lecture Notes in Computer Science (LNCS), 7884, 226-232. [More Information]
  • Surian, D., Chawla, S. (2013). Mining outlier participants: Insights using directional distributions in latent models. Lecture Notes in Computer Science (LNCS), 8190, 337-352. [More Information]
  • Pang, X., Chawla, S., Liu, W., Zheng, Y. (2013). On detection of emerging anomalous traffic patterns using GPS data. Data and Knowledge Engineering, 87, 357-373. [More Information]
  • De Vries, T., Chawla, S., Houle, M. (2012). Density-preserving projections for large-scale local anomaly detection. Knowledge and Information Systems, 32(1), 25-52. [More Information]
  • De Vries, T., Ke, H., Chawla, S., Christen, P. (2011). Robust Record Linkage Blocking Using Suffix Arrays and Bloom Filters. ACM Transactions on Knowledge Discovery from Data, 5(2), 9:1-9:27. [More Information]
  • Liu, W., Chawla, S. (2010). Mining adversarial patterns via regularized loss minimization. Machine Learning, 81(1), 69-83. [More Information]
  • Pandey, G., Chawla, S., Poon, S., Arunasalam, B., Davis, J. (2009). Association Rules Network: Definition and Applications. Statistical Analysis and Data Mining, 1(4), 260-279.
  • Verhein, F., Chawla, S. (2008). Mining spatio-temporal patterns in object mobility databases. Data Mining and Knowledge Discovery, 16(1), 5-38. [More Information]
  • McIntosh, T., Chawla, S. (2007). High-confidence rule mining for Microarray analysis. IEEE - ACM Transactions on Computational Biology and Bioinformatics, 4(4), 611-623.
  • Chawla, S., Sun, P. (2006). SLOM: a new measure for local spatial outliers. Knowledge and Information Systems, 9(4), 412-429. [More Information]
  • Chawla, S., Arunasalam, B., Davis, J. (2003). Mining Open Source Software(OSS) Data using Association Rules Network. Lecture Notes in Computer Science (LNCS), 2637, 461-466.
  • Liu, X., Shekhar, S., Chawla, S. (2003). Object-Based Directional Query Processing in Spatial Databases. IEEE Transactions On Knowledge And Data Engineering, 15(2), 295-304.
  • Shekhar, S., Lu, C., Chawla, S., Ravada, S. (2002). Efficient Join-Index-Based Spatial-Join Processing: A Clustering Approach. IEEE Transactions On Knowledge And Data Engineering, 14(6), 1400-1421.
  • Shekhar, S., Schrater, P., Vatsavai, R., Wu, W., Chawla, S. (2002). Spatial Contextual Classification and Prediction Models for Mining Geospatial Data. IEEE Transactions on Multimedia, 4(2), 174-188.
  • Liu, X., Shekhar, S., Chawla, S. (2001). Maintaining Spatial Constraints Using a Dimension Graph Approach. International Journal on Artificial Intelligence Tools, 10(4), 639-662.

Conferences

  • Pang, X., Chawla, S., Scholz, B., Wilcox, G. (2013). A Scalable Approach for LRT Computation in GPGPU Environments. 15th Asia-Pacific Web Conference on Web Technologies and Applications (APWeb 2013), Berlin: Springer. [More Information]
  • Yang, P., Liu, W., Zhou, B., Chawla, S., Zomaya, A. (2013). Ensemble-Based Wrapper Methods for Feature Selection and Class Imbalance Learning. 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Heidelberg: Springer. [More Information]
  • Chawla, S., Gionis, A. (2013). k-means--: A unified approach to clustering and outlier detection. 2013 SIAM International Conference on Data Mining (SDM13), Texas, USA: Society for Industrial and Applied Mathematics (SIAM). [More Information]
  • Wang, F., Chawla, S., Surian, D. (2013). Latent outlier detection and the low precision problem. ACM SIGKDD Workshop on Outlier Detection and Description (ODD 2013), NY, United States: Association for Computing Machinery (ACM). [More Information]
  • Menon, A., Narasimhan, H., Agarwal, S., Chawla, S. (2013). On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance. The 30th International Conference on Machine Learning (ICML 2013), online: Journal of Machine Learning Research (JMLR).
  • Wang, F., Liu, W., Chawla, S. (2013). Tikhonov or Lasso Regularization: Which Is Better and When. IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI), Piscataway, NJ, USA: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]
  • Liu, W., Chawla, S., Bailey, J., Leckie, C., Ramamohanarao, K. (2012). An Efficient Adversarial Learning Strategy for Constructing Robust Classification Boundaries. The 25th Australasian Joint Conference on Artificial Intelligence, Heidelberg: Springer. [More Information]
  • Chawla, S., Zheng, Y., Hu, J. (2012). Inferring the Root Cause in Road Traffic Anomalies. 12th IEEE International Conference on Data Mining (ICDM 2012), Los Alamitos: IEEE Computer Society. [More Information]
  • Nguyen, L., Chawla, S. (2012). Large Scale Spectral Clustering Using Resistance Distance and Spielman-Teng Solvers. The Fifteenth International Conference on Discovery Science (DS 2012), Berlin, Heidelberg: Springer Verlag. [More Information]
  • Tsang, D., Chawla, S. (2011). A Robust Index for Regular Expression Queries. 20th ACM International Conference on Information and Knowledge Managment (CIKM'11), New York, USA: Association for Computing Machinery (ACM). [More Information]
  • Liu, W., Chawla, S. (2011). Class confidence weighted kNN algorithms for imbalanced data sets. 15th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2011, Heidelberg, Germany: Springer. [More Information]
  • Liu, W., Zheng, Y., Chawla, S., Yuan, J., Xie, X. (2011). Discovering spatio-temporal causal interactions in traffic data streams. 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'11), New York, USA: Association for Computing Machinery (ACM). [More Information]
  • Pang, X., Chawla, S., Liu, W., Zheng, Y. (2011). On Mining Anomalous Patterns in Road Traffic Streams. 7th International Conference on Advanced Data Mining and Applications (ADMA 2011), Heidelberg, Germany: Springer. [More Information]
  • Liu, W., Chawla, S., Cieslak, D., Chawla, N. (2010). A Robust Decision Tree Algorithm for Imbalanced Data Sets. 2010 SIAM International Conference on Data Mining (SDM 2010), USA: Society for Industrial and Applied Mathematics (SIAM).
  • De Vries, T., Chawla, S., Houle, M. (2010). Finding Local Anomalies in Very High Dimensional Space. 10th IEEE International Conference on Data Mining (ICDM 2010), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Nguyen, L., Babaie, T., Chawla, S., Zaidi, Z. (2010). Network Anomaly Detection Using a Commute Distance Based Approach. 10th IEEE International Conference on Data Mining Workshops ICDMW 2010, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Babbar, S., Chawla, S. (2010). On Bayesian Network and Outlier Detection. 16th International Conference on Management of Data (COMAD 2010), New Delhi, India: Computer Society of India.
  • Nguyen, L., Chawla, S. (2010). Robust Outlier Detection Using Commute Time and Eigenspace Embedding. 14th Pacific-Asia Conference on Advanced in Knowledge Discovery and Data Mining, Germany: Springer.
  • Liu, W., Chawla, S. (2009). A Game Theoretical Model for Adversarial Learning. 2009 IEEE International Conference on Data Mining Workshops (ICDMW 2009), Los Alamitos: (IEEE) Institute of Electrical and Electronics Engineers.
  • De Vries, T., Ke, H., Chawla, S., Christen, P. (2009). Robust Record Linkage Blocking using Suffix Arrays. 18th ACM Conference on Information and Knowledge Management (CIKM 2009), New York: Association for Computing Machinery (ACM).
  • AL-Naymat, G., Chawla, S., Taheri, J. (2009). SparseDTW: A Novel Approach to Speed up Dynamic Time Warping. 8th Australasian Data Mining Conference AusDM 2009, Australia: Australian Computer Society.
  • Sun, P., Chawla, S., De Vries, T., Pham, G. (2008). Disk-Based Sampling for Outlier Detection in High Dimensional Data. 14th International Conference on Management of Data (COMAD 2008), India: Allied Publisher Private Limited.
  • Levy, D., Kummerfeld, R., Chawla, S., Calvo, R., Fekete, A. (2008). The New Software Engineering Program at the University of Sydney. AaeE 2008: 19th Annual Conference of the Australasian Association for Engineering Education, Rockhampton, Old: Faculty of Science, Engineering & Health, CQUniversity Australia.
  • Menon, A., Pham, G., Chawla, S., Viglas, A. (2007). An incremental data-stream sketch using sparse random projections. Seventh SIAM International Conference on Data Mining (SDM 2007), Philadelphia, USA: Society for Industrial and Applied Mathematics (SIAM).
  • AL-Naymat, G., Chawla, S., Gudmundsson, J. (2007). Dimensionality Reduction for Long Duration and Complex Spatio-Temporal Queries. 2007 ACM SIGAPP Symposium on Applied Computing (SAC '07). Association for Computing Machinery (ACM).
  • Verhein, F., Chawla, S. (2007). Using Significant, Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets. Seventh IEEE International Conference on Data Mining (ICDM 2007), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Arunasalam, B., Chawla, S. (2006). CCCS: A Top-down Associative Classifier for Imbalanced Class Distribution. Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2006), USA: Association for Computing Machinery (ACM).
  • Verhein, F., Chawla, S. (2006). Geometrically Inspired Itemset Mining. 2006 IEEE International Conference on Data Mining (ICDM 2006), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Sun, P., Chawla, S., Arunasalam, B. (2006). Mining for Outliers in Sequential Databases. The Sixth SIAM International Conference on Data Mining, USA: Society for Industrial and Applied Mathematics (SIAM).
  • Verhein, F., Chawla, S. (2006). Mining Spatio-temporal Association Rules, Sources, Sinks, Stationary Regions and Thoroughfares in Object Mobility Databases. 11th International Conference on Database Systems for Advanced Applications (DASFAA 2006), Germany: Springer.
  • Ler, D., Koprinska, I., Chawla, S. (2005). A Hill-climbing Landmarker Generation Algorithm Based on Efficiency and Correlativity Criteria. The Eighteenth International Florida Artificial Intelligence Research Society Conference - FLAIRS 2005, USA: AAAI Press.
  • Ler, D., Koprinska, I., Chawla, S. (2005). Comparisons between Heuristics Based on Correlativity and Efficiency for Landmarker Generation. Fourth International Conference on Hybrid Intelligent Systems - HIS 2004, Los Alamitos, California, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Lukov, L., Chawla, S., Church, W. (2005). Conditional Random Fields for Transmembrane Helix Prediction. The Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Berlin: Springer.
  • Ho, J., Lukov, L., Chawla, S. (2005). Sequential Pattern Mining with Constraints on Large Protein Databases. The Twelfth International Conference on Management of Data 2005, New Delhi: Allied Publisher Private Limited.
  • Arunasalam, B., Chawla, S., Sun, P. (2005). Striking Two Birds With One Stone: Simultaneous Mining of Positive and Negative Spatial Patterns. The Fifth SIAM International Conference on Data Mining, USA: Society for Industrial and Applied Mathematics (SIAM).
  • Ler, D., Koprinska, I., Chawla, S. (2005). Utilising Regression-based Landmarkers within a Meta-learning Framework for Algorithm Selection. W10 - Workshop on Meta-Learning : associated with the 22nd International Conference on Machine Learning (ICML 2005), Germany: International Machine Learning Society.
  • Ler, D., Koprinska, I., Chawla, S. (2004). A Landmarker Selection Algorithm Based On Correlation And Efficiency Criteria. 17th Australian Joint Conference on Artificial Intelligence, Berlin: Springer.
  • Ler, D., Koprinska, I., Chawla, S. (2004). A New Landmarker Generation Algorithm Based On Correlativity. The 2004 International Conference on Machine Learning and Applications (ICMLA'04), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Arunasalam, B., Chawla, S., Sun, P., Munro, R. (2004). Mining Complex Relationships In The Sdss Skyserver Spatial Database. 28th Annual International Computer Software and Applications Conference (COMPSAC 2004), Los Alamitos, CA, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Chawla, S., Davis, J., Pandey, G. (2004). On Local Pruning Of Association Rules Using Directed Hypergraphs. 20th International Conference on Data Engineering, 2004, Piscataway, NJ: (IEEE) Institute of Electrical and Electronics Engineers.
  • Sun, P., Chawla, S. (2004). On Local Spatial Outliers. 4th IEEE International Conference on Data Mining (ICDM 2004), Washington, DC, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Munro, R., Chawla, S., Sun, P. (2003). Complex spatial relationships. Third IEEE International Conference on Data Mining - ICDM, Los Alamitos: (IEEE) Institute of Electrical and Electronics Engineers.
  • Chawla, S., Shekhar, S., Wu, W. (2002). A Comparison of Markov Random Field and Spatial Regression Models for Mining Geospatial Data. The 6th Joint Conference on Information Science, USA: JCIS/ Association for Intelligent Machinery, Inc.
  • Chawla, S., Shekhar, S., Wu, W. (2001). Modeling Spatial Dependencies for Mining Geospatial Data. First SIAM International Conference on Data Mining, Philadelphia, PA: SIAM Publications.

2013

  • Babbar, S., Surian, D., Chawla, S. (2013). A Causal Approach for Mining Interesting Anomalies. Lecture Notes in Computer Science (LNCS), 7884, 226-232. [More Information]
  • Pang, X., Chawla, S., Scholz, B., Wilcox, G. (2013). A Scalable Approach for LRT Computation in GPGPU Environments. 15th Asia-Pacific Web Conference on Web Technologies and Applications (APWeb 2013), Berlin: Springer. [More Information]
  • Yang, P., Liu, W., Zhou, B., Chawla, S., Zomaya, A. (2013). Ensemble-Based Wrapper Methods for Feature Selection and Class Imbalance Learning. 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Heidelberg: Springer. [More Information]
  • Chawla, S., Gionis, A. (2013). k-means--: A unified approach to clustering and outlier detection. 2013 SIAM International Conference on Data Mining (SDM13), Texas, USA: Society for Industrial and Applied Mathematics (SIAM). [More Information]
  • Wang, F., Chawla, S., Surian, D. (2013). Latent outlier detection and the low precision problem. ACM SIGKDD Workshop on Outlier Detection and Description (ODD 2013), NY, United States: Association for Computing Machinery (ACM). [More Information]
  • Surian, D., Chawla, S. (2013). Mining outlier participants: Insights using directional distributions in latent models. Lecture Notes in Computer Science (LNCS), 8190, 337-352. [More Information]
  • Pang, X., Chawla, S., Liu, W., Zheng, Y. (2013). On detection of emerging anomalous traffic patterns using GPS data. Data and Knowledge Engineering, 87, 357-373. [More Information]
  • Menon, A., Narasimhan, H., Agarwal, S., Chawla, S. (2013). On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance. The 30th International Conference on Machine Learning (ICML 2013), online: Journal of Machine Learning Research (JMLR).
  • Wang, F., Liu, W., Chawla, S. (2013). Tikhonov or Lasso Regularization: Which Is Better and When. IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI), Piscataway, NJ, USA: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]

2012

  • Liu, W., Chawla, S., Bailey, J., Leckie, C., Ramamohanarao, K. (2012). An Efficient Adversarial Learning Strategy for Constructing Robust Classification Boundaries. The 25th Australasian Joint Conference on Artificial Intelligence, Heidelberg: Springer. [More Information]
  • De Vries, T., Chawla, S., Houle, M. (2012). Density-preserving projections for large-scale local anomaly detection. Knowledge and Information Systems, 32(1), 25-52. [More Information]
  • Chawla, S., Zheng, Y., Hu, J. (2012). Inferring the Root Cause in Road Traffic Anomalies. 12th IEEE International Conference on Data Mining (ICDM 2012), Los Alamitos: IEEE Computer Society. [More Information]
  • Nguyen, L., Chawla, S. (2012). Large Scale Spectral Clustering Using Resistance Distance and Spielman-Teng Solvers. The Fifteenth International Conference on Discovery Science (DS 2012), Berlin, Heidelberg: Springer Verlag. [More Information]

2011

  • Tsang, D., Chawla, S. (2011). A Robust Index for Regular Expression Queries. 20th ACM International Conference on Information and Knowledge Managment (CIKM'11), New York, USA: Association for Computing Machinery (ACM). [More Information]
  • Liu, W., Chawla, S. (2011). Class confidence weighted kNN algorithms for imbalanced data sets. 15th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2011, Heidelberg, Germany: Springer. [More Information]
  • Liu, W., Zheng, Y., Chawla, S., Yuan, J., Xie, X. (2011). Discovering spatio-temporal causal interactions in traffic data streams. 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'11), New York, USA: Association for Computing Machinery (ACM). [More Information]
  • Pang, X., Chawla, S., Liu, W., Zheng, Y. (2011). On Mining Anomalous Patterns in Road Traffic Streams. 7th International Conference on Advanced Data Mining and Applications (ADMA 2011), Heidelberg, Germany: Springer. [More Information]
  • De Vries, T., Ke, H., Chawla, S., Christen, P. (2011). Robust Record Linkage Blocking Using Suffix Arrays and Bloom Filters. ACM Transactions on Knowledge Discovery from Data, 5(2), 9:1-9:27. [More Information]

2010

  • Liu, W., Chawla, S., Cieslak, D., Chawla, N. (2010). A Robust Decision Tree Algorithm for Imbalanced Data Sets. 2010 SIAM International Conference on Data Mining (SDM 2010), USA: Society for Industrial and Applied Mathematics (SIAM).
  • De Vries, T., Chawla, S., Houle, M. (2010). Finding Local Anomalies in Very High Dimensional Space. 10th IEEE International Conference on Data Mining (ICDM 2010), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Liu, W., Chawla, S. (2010). Mining adversarial patterns via regularized loss minimization. Machine Learning, 81(1), 69-83. [More Information]
  • Nguyen, L., Babaie, T., Chawla, S., Zaidi, Z. (2010). Network Anomaly Detection Using a Commute Distance Based Approach. 10th IEEE International Conference on Data Mining Workshops ICDMW 2010, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Babbar, S., Chawla, S. (2010). On Bayesian Network and Outlier Detection. 16th International Conference on Management of Data (COMAD 2010), New Delhi, India: Computer Society of India.
  • Nguyen, L., Chawla, S. (2010). Robust Outlier Detection Using Commute Time and Eigenspace Embedding. 14th Pacific-Asia Conference on Advanced in Knowledge Discovery and Data Mining, Germany: Springer.
  • Wu, E., Liu, W., Chawla, S. (2010). Spatio-temporal Outlier Detection in Precipitation Data. In M M Gaber, R R Vatsavai, O A Omitaomu, J Gama, N V Chawla & A R Ganguly (Eds.), Knowledge Discovery from Sensor Data - Revised Selected Papers (LNCS 5840), (pp. 115-133). Germany: Springer.

2009

  • Liu, W., Chawla, S. (2009). A Game Theoretical Model for Adversarial Learning. 2009 IEEE International Conference on Data Mining Workshops (ICDMW 2009), Los Alamitos: (IEEE) Institute of Electrical and Electronics Engineers.
  • Pandey, G., Chawla, S., Poon, S., Arunasalam, B., Davis, J. (2009). Association Rules Network: Definition and Applications. Statistical Analysis and Data Mining, 1(4), 260-279.
  • Chawla, S., Washio, T., Minato, S., Tsumoto, S., Onoda, T., Yamada, S., Inokuchi, A. (2009). New Frontiers in Applied Data Mining: PAKDD 2008 International Workshops - LNCS Volume 5433. Germany: Springer.
  • De Vries, T., Ke, H., Chawla, S., Christen, P. (2009). Robust Record Linkage Blocking using Suffix Arrays. 18th ACM Conference on Information and Knowledge Management (CIKM 2009), New York: Association for Computing Machinery (ACM).
  • AL-Naymat, G., Chawla, S., Taheri, J. (2009). SparseDTW: A Novel Approach to Speed up Dynamic Time Warping. 8th Australasian Data Mining Conference AusDM 2009, Australia: Australian Computer Society.

2008

  • Sun, P., Chawla, S., De Vries, T., Pham, G. (2008). Disk-Based Sampling for Outlier Detection in High Dimensional Data. 14th International Conference on Management of Data (COMAD 2008), India: Allied Publisher Private Limited.
  • Verhein, F., Chawla, S. (2008). Mining spatio-temporal patterns in object mobility databases. Data Mining and Knowledge Discovery, 16(1), 5-38. [More Information]
  • Levy, D., Kummerfeld, R., Chawla, S., Calvo, R., Fekete, A. (2008). The New Software Engineering Program at the University of Sydney. AaeE 2008: 19th Annual Conference of the Australasian Association for Engineering Education, Rockhampton, Old: Faculty of Science, Engineering & Health, CQUniversity Australia.

2007

  • Menon, A., Pham, G., Chawla, S., Viglas, A. (2007). An incremental data-stream sketch using sparse random projections. Seventh SIAM International Conference on Data Mining (SDM 2007), Philadelphia, USA: Society for Industrial and Applied Mathematics (SIAM).
  • AL-Naymat, G., Chawla, S., Gudmundsson, J. (2007). Dimensionality Reduction for Long Duration and Complex Spatio-Temporal Queries. 2007 ACM SIGAPP Symposium on Applied Computing (SAC '07). Association for Computing Machinery (ACM).
  • McIntosh, T., Chawla, S. (2007). High-confidence rule mining for Microarray analysis. IEEE - ACM Transactions on Computational Biology and Bioinformatics, 4(4), 611-623.
  • Verhein, F., Chawla, S. (2007). Using Significant, Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets. Seventh IEEE International Conference on Data Mining (ICDM 2007), USA: (IEEE) Institute of Electrical and Electronics Engineers.

2006

  • Arunasalam, B., Chawla, S. (2006). CCCS: A Top-down Associative Classifier for Imbalanced Class Distribution. Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2006), USA: Association for Computing Machinery (ACM).
  • Verhein, F., Chawla, S. (2006). Geometrically Inspired Itemset Mining. 2006 IEEE International Conference on Data Mining (ICDM 2006), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Sun, P., Chawla, S., Arunasalam, B. (2006). Mining for Outliers in Sequential Databases. The Sixth SIAM International Conference on Data Mining, USA: Society for Industrial and Applied Mathematics (SIAM).
  • Verhein, F., Chawla, S. (2006). Mining Spatio-temporal Association Rules, Sources, Sinks, Stationary Regions and Thoroughfares in Object Mobility Databases. 11th International Conference on Database Systems for Advanced Applications (DASFAA 2006), Germany: Springer.
  • Chawla, S., Sun, P. (2006). SLOM: a new measure for local spatial outliers. Knowledge and Information Systems, 9(4), 412-429. [More Information]

2005

  • Ler, D., Koprinska, I., Chawla, S. (2005). A Hill-climbing Landmarker Generation Algorithm Based on Efficiency and Correlativity Criteria. The Eighteenth International Florida Artificial Intelligence Research Society Conference - FLAIRS 2005, USA: AAAI Press.
  • Ler, D., Koprinska, I., Chawla, S. (2005). Comparisons between Heuristics Based on Correlativity and Efficiency for Landmarker Generation. Fourth International Conference on Hybrid Intelligent Systems - HIS 2004, Los Alamitos, California, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Lukov, L., Chawla, S., Church, W. (2005). Conditional Random Fields for Transmembrane Helix Prediction. The Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Berlin: Springer.
  • Ho, J., Lukov, L., Chawla, S. (2005). Sequential Pattern Mining with Constraints on Large Protein Databases. The Twelfth International Conference on Management of Data 2005, New Delhi: Allied Publisher Private Limited.
  • Arunasalam, B., Chawla, S., Sun, P. (2005). Striking Two Birds With One Stone: Simultaneous Mining of Positive and Negative Spatial Patterns. The Fifth SIAM International Conference on Data Mining, USA: Society for Industrial and Applied Mathematics (SIAM).
  • Ler, D., Koprinska, I., Chawla, S. (2005). Utilising Regression-based Landmarkers within a Meta-learning Framework for Algorithm Selection. W10 - Workshop on Meta-Learning : associated with the 22nd International Conference on Machine Learning (ICML 2005), Germany: International Machine Learning Society.

2004

  • Ler, D., Koprinska, I., Chawla, S. (2004). A Landmarker Selection Algorithm Based On Correlation And Efficiency Criteria. 17th Australian Joint Conference on Artificial Intelligence, Berlin: Springer.
  • Ler, D., Koprinska, I., Chawla, S. (2004). A New Landmarker Generation Algorithm Based On Correlativity. The 2004 International Conference on Machine Learning and Applications (ICMLA'04), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Arunasalam, B., Chawla, S., Sun, P., Munro, R. (2004). Mining Complex Relationships In The Sdss Skyserver Spatial Database. 28th Annual International Computer Software and Applications Conference (COMPSAC 2004), Los Alamitos, CA, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Chawla, S., Davis, J., Pandey, G. (2004). On Local Pruning Of Association Rules Using Directed Hypergraphs. 20th International Conference on Data Engineering, 2004, Piscataway, NJ: (IEEE) Institute of Electrical and Electronics Engineers.
  • Sun, P., Chawla, S. (2004). On Local Spatial Outliers. 4th IEEE International Conference on Data Mining (ICDM 2004), Washington, DC, USA: (IEEE) Institute of Electrical and Electronics Engineers.

2003

  • Munro, R., Chawla, S., Sun, P. (2003). Complex spatial relationships. Third IEEE International Conference on Data Mining - ICDM, Los Alamitos: (IEEE) Institute of Electrical and Electronics Engineers.
  • Chawla, S., Arunasalam, B., Davis, J. (2003). Mining Open Source Software(OSS) Data using Association Rules Network. Lecture Notes in Computer Science (LNCS), 2637, 461-466.
  • Liu, X., Shekhar, S., Chawla, S. (2003). Object-Based Directional Query Processing in Spatial Databases. IEEE Transactions On Knowledge And Data Engineering, 15(2), 295-304.
  • Shekhar, S., Chawla, S. (2003). Spatial Databases: A Tour. USA: Prentice Hall.

2002

  • Chawla, S., Shekhar, S., Wu, W. (2002). A Comparison of Markov Random Field and Spatial Regression Models for Mining Geospatial Data. The 6th Joint Conference on Information Science, USA: JCIS/ Association for Intelligent Machinery, Inc.
  • Shekhar, S., Lu, C., Chawla, S., Ravada, S. (2002). Efficient Join-Index-Based Spatial-Join Processing: A Clustering Approach. IEEE Transactions On Knowledge And Data Engineering, 14(6), 1400-1421.
  • Shekhar, S., Schrater, P., Vatsavai, R., Wu, W., Chawla, S. (2002). Spatial Contextual Classification and Prediction Models for Mining Geospatial Data. IEEE Transactions on Multimedia, 4(2), 174-188.

2001

  • Liu, X., Shekhar, S., Chawla, S. (2001). Maintaining Spatial Constraints Using a Dimension Graph Approach. International Journal on Artificial Intelligence Tools, 10(4), 639-662.
  • Chawla, S., Shekhar, S., Wu, W. (2001). Modeling Spatial Dependencies for Mining Geospatial Data. First SIAM International Conference on Data Mining, Philadelphia, PA: SIAM Publications.

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