Pei Tao

Knowledge discovery from area-class resource maps: Capturing prototype effects

This paper presents a knowledge discovery approach to extracting knowledge from area-class resource maps. Prototype theory forms the basis of the approach which consists of two major components: (1) a scheme for organizing knowledge used in categorizing geographic entities which allows for the modeling of indeterminate boundaries and non-uniform memberships within categories; and (2) a data mining method using the Expectation Maximization (EM) algorithm for extracting such knowledge from area-class maps. A case study on knowledge discovery from a soil map demonstrates the details of the approach. The study shows that knowledge for classifying geographic entities with indeterminate boundaries is embedded in area-class maps and can be extracted through data mining; and that continuous spatial variation of geographic entities can be better modeled if the knowledge discovery process retains knowledge of within-class variations as well as transitions between classes.This paper presents a knowledge discovery approach to extracting knowledge from area–class resource maps. Prototype theory forms the basis of the approach which consists of two major components: (1) a scheme for organizing knowledge used in categorizing geographic entities which allows for the modeling of indeterminate boundaries and non–uniform memberships within categories; and (2) a data mining method using the Expectation Maximization (EM) algorithm for extracting such knowledge from area–class maps. A case study on knowledge discovery from a soil map demonstrates the details of the approach. The study shows that knowledge for classifying geographic entities with indeterminate boundaries is embedded in area–class maps and can be extracted through data mining; and that continuous spatial variation of geographic entities can be better modeled if the knowledge discovery process retains knowledge of within-class variations as well as transitions between classes.

The algorithm of decomposing superimposed 2-D Poisson processes and its application to the extracting earthquake clustering pattern

Aiming at the complexity of seismic gestation mechanism and spatial distribution, we hypothesize that the seismic data are composed of background earthquakes and anomaly earthquakes in a certain temporal-spatial scope. Also the background earthquakes and anomaly earthquakes both satisfy the 2-D Poisson process of different parameters respectively. In the paper, the concept ofN-th order distance is introduced in order to transform 2-D superimposed Poisson process into 1-D mixture density function. On the basis of choosing the distance, mixture density function is decomposed to recognize the anomaly earthquakes through genetic algorithm. Combined with the temporal scanning ofC value, the algorithm is applied to the recognition on spatial pattern of foreshock anomalies by examples of Songpan and Longling sequences in the southwest of China.