Downloads & Free Reading Options - Results

Temporal, Spatial, and Spatio-Temporal Data Mining: First International Workshop, TSDM 2000 Lyon, France, September 12, 2000 Revised Papers Author: John F. Roddick, Kathleen Hornsby Published by Springer Berlin Heidelberg ISBN: 978-3-540-41773-6 DOI: 10.1007/3-540-45244-3 Table 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

Temporal, Spatial, and Spatio-Temporal Data Mining: First International Workshop, TSDM 2000 Lyon, France, September 12, 2000 Revised Papers Author: John F. Roddick, Kathleen Hornsby Published by Springer Berlin Heidelberg ISBN: 978-3-540-41773-6 DOI: 10.1007/3-540-45244-3 Table 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

Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.