ITLApplied  Computational Mathematics Division
ACMD Seminar Series
Attractive Image NIST

Data Mining on Spatial Data

Qin Ding
Department of Computer Science, Penn State Harrisburg

Tuesday, February 25, 2003 14:00-15:00,
Room 145, NIST North (820)
Tuesday, February 25, 2003 12:00-13:00,
Room 4550

Abstract: The progress of data-collection technology, such as bar-code scanners in commercial domains and sensors in scientific and industrial domains, generates massive amounts of data. This explosive growth in data generates the need for new techniques and tools that can intelligently and automatically transform the data into useful information and knowledge. Data mining, also referred to as knowledge discovery in databases (KDD), is a process of nontrivial extraction of implicit, previous unknown and potentially useful information (such as knowledge rules, constrains, regularities) from data in databases. Various data mining techniques have been proposed, including association rule mining, classification, clustering, etc. Data mining techniques have been applied to many areas, such as market basket data, web data, DNA data, text data, and spatial data. Extracting interesting patterns and rules from spatial datasets, such as remotely sensed imagery and associated ground data, can be of importance in precision agriculture, community planning, resource discovery and other areas. However, in most cases the image data sizes are too large to be mined in a reasonable amount of time using existing algorithms. In this seminar, we will introduce approaches to perform efficient and effective data mining (including association rule mining, classification, and clustering) on spatial data using Peano Count Tree (P-tree) structure. P-tree structure facilitates significant pruning techniques and proves to be a promising approach to spatial data mining.
Contact: J. E. Terrill

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