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Professor Sanjay Chawla

PhD (University of Tennessee)
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

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

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Selected grants

2013

  • 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 Project.

2007

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

2005

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

2003

  • Determining outlier patterns in health data; Hale R, Chawla S; Capital Markets CRC/BLO Project.
  • Extraction and Management of Spatially indexed time series; Chawla S; University of Sydney (Sesqui)/New Staff Support Scheme.

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.
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Selected publications

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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

  • 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.
  • 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.
  • Liu, W., Chawla, S. (2010). Mining adversarial patterns via regularized loss minimization. Machine Learning, 81(1), 69-83.
  • 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.
  • 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.
  • 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

  • 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-Verlag.
  • Chawla, S., Zheng, Y., Hu, J. (2012). Inferring the Root Cause in Road Traffic Anomalies. 12th IEEE International Conference on Data Mining (ICDM 2012), Piscataway: (IEEE) Institute of Electrical and Electronics Engineers.
  • 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.
  • 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).
  • 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.
  • 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).
  • 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.
  • 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.

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-Verlag.
  • 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.
  • Chawla, S., Zheng, Y., Hu, J. (2012). Inferring the Root Cause in Road Traffic Anomalies. 12th IEEE International Conference on Data Mining (ICDM 2012), Piscataway: (IEEE) Institute of Electrical and Electronics Engineers.
  • 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.

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).
  • 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.
  • 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).
  • 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.
  • 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.

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.
  • 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.
  • 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.

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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