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This volume contains updated versions of the ten papers presented at the First International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining (TSDM 2000) held in conjunction with the 4th European Conference on Prin- ples and Practice of Knowledge Discovery in Databases (PKDD 2000) in Lyons, France in September, 2000. The aim of the workshop was to bring together experts in the analysis of temporal and spatial data mining and knowledge discovery in temporal, spatial or spatio-temporal database systems as well as knowledge engineers and domain experts from allied disciplines. The workshop focused on research and practice of knowledge discovery from datasets containing explicit or implicit temporal, spatial or spatio-temporal information. The ten original papers in this volume represent those accepted by peer review following an international call for papers. All papers submitted were refereed by an international team of data mining researchers listed below. We would like to thank the team for their expert and useful help with this process. Following the workshop, authors were invited to amend their papers to enable the feedback received from the conference to be included in the ?nal papers appearing in this volume. A workshop report was compiled by Kathleen Hornsby which also discusses the panel session that was held.
List of contents
Workshop Report - International Workshop on Temporal, Spatial, and Spatio-temporal Data Mining - TSDM2000.- Discovering Temporal Patterns in Multiple Granularities.- Refined Time Stamps for Concept Drift Detection During Mining for Classification Rules.- K-Harmonic Means -A Spatial Clustering Algorithm with Boosting.- Identifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events.- Value Range Queries on Earth Science Data via Histogram Clustering.- Fast Randomized Algorithms for Robust Estimation of Location.- Rough Sets in Spatio-temporal Data Mining.- Join Indices as a Tool for Spatial Data Mining.- Data Mining with Calendar Attributes.- AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles.- An Updated Bibliography of Temporal, Spatial, and Spatio-temporal Data Mining Research.
