Computer technologies have been introduced into the area of agriculture recently. Precision
agriculture, as an example, is a popular concept of using GIS, GPS and other new technologies in
helping farmers optimize agricultural production. Colocation pattern mining is a technique for
discovering relationships between different thematic features in a spatial domain. For example, an
observation that large cities are often close to riversides is obtained with a reliable statistic. Such
desired capability is of importance in agricultural applications, like insect pest management. In this
paper, a two-phase hierarchical clustering method is proposed to assist people in making decisions
based on spatial colocation patterns implicitly existing inside the geographical data sets. It is designed
to be a generic system for any data sets in point format. In the first phase, the point features being close
together are grouped into a number of clusters. An LC matrix is generated to describe the relationship
between the clusters and the layers of feature points. The LC matrix is then analyzed by the second
hierarchical clustering to generate a dendrogram. The support and confidence of each single cluster in
the dendrogram are calculated to show the concurrent occurrence of features, regardless of their
geographical locations.
Relation:
Asian Journal of Health and Information Sciences 1(1):61-72
