An introduction to contemporary statistics (2nd ed.)
An introduction to contemporary statistics (2nd ed.)
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Statistical and scientific databases
Statistical and scientific databases
Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Optimal composition of real-time systems
Artificial Intelligence
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Knowledge discovery methods analyse data of quite diverse types such as cross sections, time series, texts, or multimedia data. We first summarize dimensions being important for identifying subtypes of these main general data types that require special analysis approaches. Thus, specifically for complex, that is, large, high-dimensional, multirelational, or dynamic, data sets problems occur with current views on data developed in statistics and other disciplines. Next, we deal with some organizational forms. Data are not collected as an unregulated set of numbers or strings, but appear in some organized form. Starting at the classical form used in data analysis for organizing cross-sectional data, the flat rectangular organization, we proceed to more complex forms and deal with time and space referenced data. Finally, we treat data aggregation levels and meta data. A classification of data types is useful to select appropriate mining tasks, analysis methods, and data management solutions for an application.