BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Histogram clustering for unsupervised segmentation and image retrieval
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Virtual Domain Application Data Center: Serving Interdisciplinary Earth Scientists
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
A Pyramid Data Model for Supporting Content-Based Browsing and Knowledge Discovery
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Fast Approximate Answers to Aggregate Queries on a Data Cube
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Techniques for Online Exploration of Large Object-Relational Datasets
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Range Selectivity Estimation for Continuous Attributes
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Remote Data Access via the SIESIP Distributed Information System
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
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Remote sensing data as well as ground-based and model output data about the Earth system can be very large in volume. On the other hand, in order to use the data efficiently, scientists need to search for data based on not only metadata but also actual data values. To answer value range queries by scanning very large volumes of data is obviously unrealistic. This article studies a clustering technique on histograms of data values on predefined cells to index the cells. Through this index system, the so-called statistical range queries can be answered quickly and approximately together with an accuracy assessment. Examples of using this technique for Earth science data sets are given in this article.