These papers have been collected by John Roddick, Kathleen Hornsby and Myra Spiliopoulou and represent a superset of those papers contained in the bibliographies which appeared in ACM SIGKDD Explorations, Vol.1, Issue 1 and in Springer's Lecture Notes in Artificial Intelligence series, Vol 2007.
The papers in this bibliography are listed in alphabetical order by first author within the major areas of interest. Additions are gratefully received - please send to either John, Kathleen or Myra.
Last updated Thursday, 15 November 2001.
For a copy of the original SIGKDD bibliography click here, for the later Springer bibliography here and for the SIGKDD Workshop version (the latest) here. Finally, for a BibTeX formatted version of the collection, click here
To add a new entry, please complete the form here.
Berger, G. and Tuzhilin, A. (1998). Discovering unexpected patterns in temporal data using temporal logic. Temporal Databases - Research and Practice. Berlin, Springer-Verlag. Lecture Notes in Computer Science. 1399. Etzion, O., Jajodia, S. and Sripada, S., Eds. 281-309.
Black, M. M. and Hickey, R. J. (1999). Maintaining the Performance of a Learned Classifier under Concept Drift. Intelligent Data Analysis 3(6): 453-474.
Chen, X. and Petrounias, I. (1998). A framework for temporal data mining. In Proc. Ninth International Conference on Database and Expert Systems Applications, DEXA'98, Vienna, Austria. Lecture Notes in Computer Science, 1460. Quirchmayr, G., Schweighofer, E. and Bench-Capon, T. J. M., Eds., Springer-Verlag. 796-805.
Fawcett, T. and Provost, F. (1999). Activity Monitoring: Noticing Interesting Changes in Behaviour. In Proc. Fifth International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA. Chaudhuri, S. and Madigan, D., Eds., ACM Press. 53-62.
Malerba, D., Esposito, F. and Lisi, F. A. (2001). A Logical Framework for Frequent Pattern Discovery in Spatial Data. In Proc. 14th International FLAIRS Conference, Key West, Florida. Russell, I. and Kolen, J., Eds., AAAI Press. 557-561.
Peuquet, D. J. (1994). It's about Time: A Conceptual Framework for the Representation of Spatiotemporal Dynamics in Geographic Information Systems. Annals of the Association of American Geographers 84: 441-461.
Rainsford, C. P. and Roddick, J. F. (1996). Temporal data mining in information systems: a model. In Proc. Seventh Australasian Conference on Information Systems, Hobart, Tasmania. 2. 545-553.
Saraee, M. H. and Theodoulidis, B. (1995). Knowledge discovery in temporal databases: The initial step. In Proc. DOOD'95 Post-Conference Workshop ``Knowledge Discovery in Databases and DOOD'', Singapore. Ong, K., Conrad, S. and Ling, T. W., Eds., 17-22.
Spiliopoulou, M. and Roddick, J. F. (2000). Higher Order Mining: Modelling and Mining the Results of Knowledge Discovery. Data Mining II - Proc. Second International Conference on Data Mining Methods and Databases. Cambridge, UK, WIT Press. Ebecken, N. and Brebbia, C. A., Eds. 309-320. Text Available.
Chen, X., Petrounias, I. and Heathfield, H. (1998). Discovering temporal association rules in temporal databases. In Proc. International Workshop on Issues and Applications of Database Technology (IADT'98). 312-319.
Chen, X. and Petrounias, I. (1999). Mining Temporal Features in Association Rules. In Proc. 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'99), Prague. Lecture Notes in Artificial Intelligence, 1704. Zytkow, J. M. and Rauch, J., Eds., Springer. 295-300.
Chen, X. and Petrounias, I. (2000). An Integrated Query and Mining System for Temporal Association Rules. In Proc. Second International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2000), London, UK. Lecture Notes in Computer Science, 1874. Kambayashi, Y., Mohania, M. K. and Tjoa, A. M., Eds., Springer. 327-336.
Chen, X. and Petrounias, I. (2000). Discovering Temporal Association Rules: Algorithms, Language and System. In Proc. Sixteenth International Conference on Data Engineering (ICDE2000), San Diego, California, USA. IEEE Computer Society. 306.
Estivill-Castro, V. and Murray, A. T. (1998). Discovering associations in spatial data-an efficient mediod based approach. In Proc. Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining, PAKDD-98. Springer-Verlag, Berlin. 110-121.
Koperski, K. and Han, J. (1995). Discovery of Spatial Association Rules in Geographic Information Databases. In Proc. Fourth International Symposium on Large Spatial Databases, Maine. 47-66.
Miller, R. J. and Yang, Y. (1997). Association Rules over Interval Data. In Proc. ACM SIGMOD Conference on the Management of Data, Tucson, Arizona, USA. Peckham, J., Ed. ACM Press. 452-461.
Rainsford, C. P. and Roddick, J. F. (1999). Adding Temporal Semantics to Association Rules. In Proc. 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'99), Prague. Lecture Notes in Artificial Intelligence, 1704. Zytkow, J. M. and Rauch, J., Eds., Springer. 504-509. Text Available.
Salleb, A. and Vrain, C. (2000). An Application of Association Rules Discovery to Geographic Information Systems. In Proc. 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'2000, Lyon, France. Lecture Notes in Artificial Intelligence, 1910. Zighed, D. A., Komorovski, J. and Zytkow, J., Eds., Springer. 613-618. Text Available.
Shen, L. and Shen, H. (1998). Mining Flexible Multiple-Level Association Rules in All Concept Hierarchies (Extended Abstract). In Proc. Ninth International Conference on Database and Expert Systems Applications, DEXA'98, Vienna, Austria. Lecture Notes in Computer Science, 1460. Quirchmayr, G., Schweighofer, E. and Bench-Capon, T. J. M., Eds., Springer-Verlag. 786-795.
Wang, W., Yang, J. and Muntz, R. R. (2001). TAR: Temporal Association Rules on Evolving Numerical Attributes. In Proc. Seventeenth International Conference on Data Engineering, ICDE 2001, Heidelberg, Germany. IEEE Computer Society. 283-292.
Ye, S. and Keane, J. A. (1998). Mining association rules in temporal databases. In Proc. International Conference on Systems, Man and Cybernetics. IEEE, New York. 2803-2808.
Agrawal, R. and Srikant, R. (1995). Mining sequential patterns. In Proc. Eleventh International Conference on Data Engineering, Taipei, Taiwan. Yu, P. S. and Chen, A. S. P., Eds., IEEE Computer Society Press. 3-14.
Bayardo Jr, R. J. (1998). Efficiently mining long patterns from databases. In Proc. ACM SIGMOD Conference on the Management of Data, Seattle, WA, USA. ACM Press. 85-93.
Berger, G. and Tuzhilin, A. (1998). Discovering unexpected patterns in temporal data using temporal logic. Temporal Databases - Research and Practice. Berlin, Springer-Verlag. Lecture Notes in Computer Science. 1399. Etzion, O., Jajodia, S. and Sripada, S., Eds. 281-309.
Bettini, C., Wang, X. S., Jajodia, S. and Lin, J.-L. (1998). Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences. IEEE Transactions on Knowledge and Data Engineering 10(2): 222-237.
Chen, M. S., Park, J. S. and Yu, P. S. (1996). Data mining for path traversal patterns in a web environment. In Proc. Sixteenth International Conference on Distributed Computing Systems. 385-392.
Dietterich, T. G. and Michalski, R. S. (1985). Discovering patterns in sequences of events. Artificial Intelligence 25: 187-232.
Dousson, C. and Duong, T.V. (1999). Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems.. In Proc. Sixteenth International Joint Conference on Artificial Intelligence, Stockholm, Sweden. Dean, T., Ed. Morgan Kaufmann. 620-626.
Han, J., Gong, W. and Yin, Y. (1998). Mining segment-wise periodic patterns in time-related databases. In Proc. Fourth International Conference on Knowledge Discovery and Data Mining. AAAI Press, Menlo Park. 214-218.
Kam, P.-S. and Fu, A. W.-C. (2000). Discovering Temporal Patterns for Interval-Based Events. In Proc. Second International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2000), London, UK. Lecture Notes in Computer Science, 1874. Kambayashi, Y., Mohania, M. K. and Tjoa, A. M., Eds., Springer. 317-326.
Klosgen, W. (1995). Deviation and association patterns for subgroup mining in temporal, spatial, and textual data bases. In Proc. First International Conference on Rough Sets and Current Trends in Computing, RSCTC'98. Springer-Verlag, Berlin,. 1-18.
Lesh, N., Zaki, M. J. and Ogihara, M. (1999). Mining Features for Sequence Classification. In Proc. Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego. Chaudhuri, S. and Madigan, D., Eds., ACM Press.
Li, Y., Wang, X. S. and Jajodia, S. (2000). Discovering Temporal Patterns in Multiple Granularities. In Proc. International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000, Lyon, France. Lecture Notes in Artificial Intelligence, 2007. Roddick, J. F. and Hornsby, K., Eds., Springer.
Lin, D.-I. and Kedem, Z. M. (1998). Pincer Search: A new algorithm for discovering the maximum frequent set. In Proc. International Conference on Extending Database Technology, EDBT'98, Valencia, Spain. 385-392.
Mannila, H., Toivonen, H. and Verkamo, A. I. (1995). Discovering frequent episodes in sequences. In Proc. First International Conference on Knowledge Discovery and Data Mining (KDD-95), Montreal, Quebec, Canada. Fayyad, U., M. and Uthurusamy, R., Eds., AAAI Press, Menlo Park, CA, USA. 210-215.
Mannila, H. and Toivonen, H. (1996). Discovering generalised episodes using minimal occurences. In Proc. Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, Oregon. AAAI Press, Menlo Park. 146-151.
Ong, K. L., Conrad, S. and Ling, T. W., Eds. (1995). Knowledge Discovery and Temporal Reasoning in Deductive and Object-Oriented Databases. Proceedings of the DOOD'95 Post-Conference Workshops on Integration of Knowledge Discovery in Databases with Deductive and Object-Oriented Databases (KDOOD) and Temporal Reasoning in Deductive and Object-Oriented Databases (TDOOD). Singapore.
Padmanabhan, B. and Tuzhilin, A. (1996). Pattern discovery in temporal databases: a temporal logic approach. In Proc. Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon. Simoudis, E., Han, J. and Fayyad, U., Eds., AAAI Press.
Povinelli, R. (2000). Identifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events. In Proc. International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000, Lyon, France. Lecture Notes in Artificial Intelligence, 2007. Roddick, J. F. and Hornsby, K., Eds., Springer.
Srikant, R. and Agrawal, R. (1996). Mining sequential patterns: generalisations and performance improvements. In Proc. International Conference on Extending Database Technology, EDBT'96, Avignon, France. Lecture Notes in Computer Science, 1057. Peter M. G. Apers, Bouzeghoub, M. and Gardarin, G., Eds., Springer-Verlag. 3-17.
Tuzhilin, A. and Padmanabhan, B. (1996). Pattern Discovery in Temporal Databases: A Temporal Logic Approach. In Proc. 2nd International Conference on Knowledge Discovery and Data Mining, KDD'96, Portland, OR. Simoudis, E., Han, J. and Fayyad, U., Eds., AAAI Press. 351-354.
Wade, T. D., Byrns, P. J., Steiner, J. F. and Bondy, J. (1994). Finding temporal patterns - a set based approach. Artificial Intelligence in Medicine 6: 263-271.
Wang, K. and Tan, J. (1996). Incremental discovery of sequential patterns. In Proc. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, Canada.
Wang, K. (1997). Discovering patterns from large and dynamic sequential data. Intelligent Information Systems 8: 8-33.
Weiss, G. M. and Hirsh, H. (1998). Learning to predict rare events in event sequences. In Proc. Fourth International Conference on Knowledge Discovery and Data Mining (KDD'98), New York, NY. Agrawal, R., Stolorz, P. and Piatetsky-Shapiro, G., Eds., AAAI Press, Menlo Park, CA. 359-363.
Wexelblat, A. (1996). An environment for aiding information-browsing tasks. In Proc. AAAI Spring Symposium on Acquisition, Learning and Demonstration: Automating Tasks for Users, Birmingham, England. AAAI Press.
Wijsen, J. and Meersman, R. (1997). On the Complexity of Mining Temporal Trends. In Proc. SIGMOD'97 Workshop on Research Issues on Data Mining and Knowledge Discovery, DMDK'97, Technical Report 97-07, Tucson, AZ. Ng, R., Ed. ACM Press. 77-84.
Zaki, M. J. (1998). Efficient Enumeration of Frequent Sequences. In Proc. Seventh International Conference on Information and Knowledge Management, Washington DC. 68-75.
Zaki, M. J., Lesh, N. and Ogihara, M. (1998). PlanMine: Sequence mining for plan failures. In Proc. Fourth International Conference on Knowledge Discovery and Data Mining (KDD'98), New York, NY. Agrawal, R., Stolorz, P. and Piatetsky-Shapiro, G., Eds., ACM Press. 369-373. A more detailed version appears in Artificial Intelligence Review, special issue on the Application of Data Mining, 1999.
Zaki, M. J., Lesh, N. and Ogihara, M. (1999). PlanMine: Predicting Plan Failures using Sequence Mining. Artificial Intelligence Review, special issue on the Application of Data Mining.
Berndt, D. J. and Clifford, J. (1995). Finding patterns in time series: a dynamic programming approach. Advances in Knowledge Discovery and Data Mining. AAAI Press/ MIT Press. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P. and Uthurusamy, R., Eds. 229-248.
Clifford, J., Dhar, V. and Tuzhilin, A. (1995). Knowledge Discovery from Databases: The NYU Project. Technical Report IS-95-12. New York University New York.
Faloutsos, C., Ranganathan, M. and Manolopoulos, Y. (1994). Fast subsequence matching in time-series databases. In Proc. ACM SIGMOD Conference on the Management of Data, Minneapolis, USA. 419-429.
Guralnik, V. and Srivastava, J. (1999). Event Detection from Time Series Data. In Proc. Fifth International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA. Chaudhuri, S. and Madigan, D., Eds., ACM Press. 33-42.
Han, J., Dong, G. and Yin, Y. (1999). Efficient Mining of Partial Periodic Patterns in Time Series Database. In Proc. Fifteenth International Conference on Data Engineering, Sydney, Australia. IEEE Computer Society. 106-115.
Keogh, E. and Smyth, P. (1997). A probabilistic approach to fast pattern matching in time series databases. In Proc. Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, USA. Heckerman, D., Mannila, H., Pregibon, D. and Uthurusamy, R., Eds., AAAI Press, Menlo Park, California. 24-30.
Keogh, E. J. and Pazzani, M. (1999). An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. In Proc. 11th International Conference on Scientific and Statistical Database Management, SSDBM'99, Cleveland, OH. IEEE Computer Society. 56-67.
Keogh, E. J. and Pazzani, M. J. (2000). A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. In Proc. Knowledge Discovery and Data Mining, Current Issues and New Applications, 4th Pacific-Asia Conference, PAKDD 2000, Kyoto, Japan. Lecture Notes in Computer Science, 1805. Terano, T., Liu, H. and Chen, A., Eds., Springer. 122-133.
Kim, E., Lam, J. M. W. and Han, J. (2000). AIM: Approximate Intelligent Matching for Time Series Data. In Proc. Second International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2000), London, UK. Lecture Notes in Computer Science, 1874. Kambayashi, Y., Mohania, M. K. and Tjoa, A. M., Eds., Springer. 347-357.
Povinelli, R. (2000). Identifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events. In Proc. International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000, Lyon, France. Lecture Notes in Artificial Intelligence, 2007. Roddick, J. F. and Hornsby, K., Eds., Springer.
Tsumoto, S. (1999). Rule Discovery in Large Time-Series Medical Databases. In Proc. Principles of Data Mining and Knowledge Discovery, Third European Conference, PKDD '99, Prague, Czech Republic. Lecture Notes in Computer Science, 1704. Zytkow, J. and Rauch, J., Eds., Springer. 23-31.
Abraham, T. and Roddick, J. F. (1999). Incremental meta-mining from large temporal data sets. Advances in Database Technologies, Proc. First International Workshop on Data Warehousing and Data Mining, DWDM'98. Berlin, Springer-Verlag. Lecture Notes in Computer Science. 1552. Kambayashi, Y., Lee, D. K., Lim, E.-P., Mohania, M. and Masunaga, Y., Eds. 41-54.
Agrawal, R. and Psaila, G. (1995). Active Data Mining. In Proc. First International Conference on Knowledge Discovery and Data Mining (KDD-95), Montreal, Quebec, Canada. Fayyad, U., M. and Uthurusamy, R., Eds., AAAI Press, Menlo Park, CA, USA. 3-8.
Bettini, C., Wang, X. S. and Jajodia, S. (1996). Testing Complex Temporal Relationships Involving Multiple Granularities and its Application to Data Mining. In Proc. 15th ACM SIGACT-SIGMOD-SIGART Symposium on the Principles of Database Systems, Montreal, Canada. ACM Press. 68-78.
Bettini, C., Wang, X. S. and Jajodia, S. (1998). Mining Temporal Relationships with Multiple Granularities in Time Sequences. Data Engineering Bulletin 21(1): 32-38.
Blum, R. L. (1982). Discovery and Representation of Causal Relationships from a Large Time-Oriented Clinical Database: The {RX} Project. Lecture Notes in Medical Informatics. Springer-Verlag. 19.
Blum, R. L. (1982). Discovery, Confirmation and Interpretation of Causal Relationships from a Large Time-Oriented Clinical Database: The {RX} Project. Computers and Biomedical Research 15(2): 164-187.
Chakrabarti, S., Sarawagi, S. and Dom, B. (1998). Mining surprising patterns using temporal description length. In Proc. Twenty-Fourth International Conference on Very Large databases VLDB'98, New York, NY. Gupta, A., Shmueli, O. and Widom, J., Eds., Morgan Kaufmann. 606-617.
Chen, X. and Petrounias, I. (1998). Language support for temporal data mining. In Proc. Second European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD'98, Nantes, France. Lecture Notes in Computer Science, 1510. Zytkow, J. M. and Quafalou, M., Eds., Springer-Verlag, Berlin. 282-290.
Chen, X., Petrounias, I. and Heathfield, H. (1998). Discovering temporal association rules in temporal databases. In Proc. International Workshop on Issues and Applications of Database Technology (IADT'98). 312-319.
Hamilton, H. J. and Randall, D. J. (1999). Heuristic Selection of Aggregated Temporal Data for Knowledge Discovery. In Proc. Multiple Approaches to Intelligent Systems, 12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE-99,, Cairo, Egypt. Lecture Notes in Computer Science, 1611. Imam, I., Kodratoff, Y., El-Dessouki, A. and Ali, M., Eds., Springer. 714-723.
Hamilton, H. J. and Randall, D. J. (2000). Data Mining with Calendar Attributes. In Proc. International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000, Lyon, France. Lecture Notes in Artificial Intelligence, 2007. Roddick, J. F. and Hornsby, K., Eds., Springer.
Hickey, R. and Black, M. M. (2000). Refined Time Stamps for Concept Drift Detection During Mining for Classification Rules. In Proc. International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000, Lyon, France. Lecture Notes in Artificial Intelligence, 2007. Roddick, J. F. and Hornsby, K., Eds., Springer.
Hoeppner, F. (2001). Learning Temporal Rules from State Sequences. In Proc. IJCAI Workshop on Learning from Temporal and Spatial Data, Seattle, USA. Text Available.
Imam, I. F. (1994). An experimental study of discovery in large temporal databases. In Proc. Seventh International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE '94. 171-180.
Keogh, E. and Pazzani, M. (1999). Scaling up Dynamic Time Warping to Massive Datasets. In Proc. 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'99), Prague, Czech Republic. Lecture Notes in Artificial Intelligence, 1704. Zytkow, J. M. and Rauch, J., Eds., Springer. 1-11.
Lin, W. and Orgun, M. A. (2000). Temporal Data Mining Using Hidden Periodicity Analysis. In Proc. Twelfth International Symposium on the Foundations of Intelligent Systems, ISMIS 2000, Charlotte, NC, USA. Lecture Notes in Computer Science, 1932. Ras, Z. W. and Ohsuga, S., Eds., Springer. 49-58.
Lin, W., Orgun, M. A. and Williams, G. J. (2000). Temporal Data Mining Using Multilevel-Local Polynominal Models. In Proc. 2nd International Conference on Intelligent Data Engineering and Automated Learning, (IDEAL 2000), Shatin, N.T., Hong Kong. Lecture Notes in Computer Science, 1983. Springer. 180-186. Text Available.
Lin, W., Orgun, M. A. and Williams, G. J. (2001). Temporal Data Mining Using Hidden Markov-Local Polynomial Models. In Proc. 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining - PAKDD 2001, Hong Kong, China. Lecture Notes in Computer Science, 2035. Cheung, D. W.-L., Williams, G. J. and Li, Q., Eds., Springer. 324-335.
Long, J. M., Irani, E. A. and Slagle, J. R. (1991). Automating the Discovery of Causal Relationships in a Medical Records Database. Knowledge discovery in databases. AAAI Press/MIT Press. Piatetsky-Shapiro, G. and Frawley, W. J., Eds. 465-476.
Rainsford, C. P. and Roddick, J. F. (1997). An attribute-oriented induction of rules from temporal interval data. In Proc. Eighth International Database Workshop, Data Mining, Data Warehousing and Client/Server Databases (IDW'97), Hong Kong. Fong, J., Ed. Springer Verlag. 108-118.
Rainsford, C. P. and Roddick, J. F. (2000). Temporal Interval Logic in Data Mining. In Proc. Sixth Pacific Rim International Conference on Artificial Intelligence, PRICAI2000, Melbourne. Lecture Notes in Artificial Intelligence, 1886. Mizoguchi, R. and Slaney, J., Eds., Springer. 798.
Rainsford, C. P. and Roddick, J. F. (2000). Visualisation of Temporal Interval Association Rules. In Proc. 2nd International Conference on Intelligent Data Engineering and Automated Learning, (IDEAL 2000), Shatin, N.T., Hong Kong. Lecture Notes in Computer Science, 1983. Springer. 91-96. Text Available.
Saraee, M. H. and Theodoulidis, B. (1995). Knowledge discovery in temporal databases. In Proc. IEE Colloquium on 'Knowledge Discovery in Databases'. IEE, London. 1-4.
Sasisekharan, R., Seshadri, V. and Weiss, S. M. (1996). Data Mining and Forecasting in Large-Scale Telecommunication Networks. IEEE Expert 11(1): 37-43.
Ankerst, M., Breunig, M., Kriegel, H.-P. and Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. In Proc. ACM SIGMOD International Conference on the Management of Data, Philadelphia, PA, USA. ACM. 49-60.
Ankerst, M., Kastenmüller, G., Kriegel, H.-P. and Seidl, T. (1999). 3D Shape Histograms for Similarity Search and Classification in Spatial Databases. In Proc. Advances in Spatial Databases, 6th International Symposium, SSD'99, Hong Kong, China. 1651. Güting, R., Papadias, D. and Lochovsky, F., Eds., Springer. 207-228.
Bell, D. A., Anand, S. S. and Shapcott, C. M. (1994). Data Mining in Spatial Databases. In Proc. International Workshop on Spatio-Temporal Databases, Benicassim, Spain.
Bigolin, N. M. and Marsala, C. (1998). Fuzzy Spatial OQL for Fuzzy Knowledge Discovery in Databases. In Proc. Second European Symposium on the Principles of Data Mining and Knowledge Discovery, PKDD'98, Nantes, France. Lecture Notes in Computer Science, 1510. Zytkow, J. M. and Quafalou, M., Eds., Springer-Verlag, Berlin. 246-254.
Chawla, S., Shekhar, S., Wu, W. and Ozesmi, U. (2000). Extending Data Mining for Spatial Applications: A Case Study in Predicting Nest Locations. In Proc. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Texas, USA. Gunopulos, D. and Rastogi, R., Eds., ACM. 70-77.
Ester, M., Kriegel, H.-P. and Xu, X. (1995). Knowledge discovery in large spatial databases: focusing techniques for efficient class identification. In Proc. Advances in Spatial Databases, 4th International Symposium, SSD'95, Portland, ME. Lecture Notes in Computer Science, 951. Egenhofer, M. and Herring, J., Eds., Springer. 67-82.
Ester, M., Kriegel, H.-P. and Xu, X. (1995). A Database Interface for Clustering in Large Spatial Databases. In Proc. First International Conference on Knowledge Discovery and Data Mining, KDD'95, Montreal, Canada. AAAI Press. 94-99.
Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proc. Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon. Simoudis, E., Han, J. and Fayyad, U., Eds., AAAI Press. 226-231.
Ester, M., Kriegel, H.-P. and Sander, J. (1997). Spatial Data Mining: A Database Approach. In Proc. Fifth Symposium on Large Spatial Databases (SSD'97), Berlin, Germany. Lecture Notes in Computer Science, 1262. Scholl, M. and Voisard, A., Eds., Springer. 48-66.
Ester, M., Frommelt, A., Kriegel, H. P. and Sander, J. (1998). Algorithms for characterization and trend detection in spatial databases. In Proc. Fourth International Conference on Knowledge Discovery and Data Mining., New York, NY. Agrawal, R., Stolorz, P. and Piatetsky-Shapiro, G., Eds., AAAI Press, Menlo Park. 44-50.
Ester, M. and Wittmann, R. (1998). Incremental Generalization for Mining in a Data Warehousing Environment. In Proc. Sixth International Conference on Extending Database Technology, Valencia, Spain. Lecture Notes in Computer Science, 1377. Schek, H.-J., Saltor, F. and Ramos, I., Eds., Springer. 135-149.
Ester, M., Gundlach, S., Kriegel, H.-P. and Sander, J. (1999). Database Primitives for Spatial Data Mining. In Proc. International Conference on Databases in Office, Engineering and Science, BTW'99, Freiberg, Germany. 137-150.
Ester, M., Kriegel, H.-P. and Sander, J. (1999). Knowledge Discovery in Spatial Databases. In Proc. 23rd German Conference on Artificial Intelligence, KI'99, Bonn, Germany. Lecture Notes in Computer Science, 1701. Springer. 61-74.
Ester, M., Frommelt, A., Kriegel, H.-P. and Sander, J. (2000). Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support. Data Mining and Knowledge Discovery 4(2/3): 193-216.
Estivill-Castro, V. and Murray, A. T. (1998). Discovering associations in spatial data-an efficient mediod based approach. In Proc. Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining, PAKDD-98. Springer-Verlag, Berlin. 110-121.
Estivill-Castro, V. and Houle, M. (2000). Fast Randomized Algorithms for Robust Estimation of Location. In Proc. International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000, Lyon, France. Lecture Notes in Artificial Intelligence, 2007. Roddick, J. F. and Hornsby, K., Eds., Springer.
Estivill-Castro, V. and Lee, I. (2000). AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles. In Proc. International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000, Lyon, France. Lecture Notes in Artificial Intelligence, 2007. Roddick, J. F. and Hornsby, K., Eds., Springer.
Han, J., Koperski, K. and Stefanovic, N. (1997). GeoMiner: A System Prototype for Spatial Data Mining. In Proc. ACM SIGMOD Conference on the Management of Data, Tucson, Arizona, USA. Peckham, J., Ed. ACM Press. 553-556.
Han, J., Stefanovic, N. and Koperski, K. (1998). Selective Materialization: An Efficient Method for Spatial Data Cube Construction. In Proc. Research and Development in Knowledge Discovery and Data Mining, Second Pacific-Asia Conference, PAKDD'98, Melbourne, Australia. Wu, X., Ramamohanarao, K. and Korb, K., Eds., 144-158.
Kang, I.-S., Kim, T.-W. and Li, K.-J. (1997). A Spatial Data Mining Method by Delaunay Triangulation. In Proc. Fifth ACM Workshop on Geographic Information Systems, GIS'97, Las Vegas, Nevada. 35-39.
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