Abstract
Pattern mining is an important task in AI for eliciting hypotheses from the data. When it comes to spatial data, the geo-coordinates are often considered independently as two different attributes. Consequently, rectangular patterns are searched for. Such an arbitrary form is not able to capture interesting regions in general. We thus introduce convex polygons, a good trade-off for capturing high density areas in any pattern mining task. Our contribution is threefold: (i) We formally introduce such patterns in Formal Concept Analysis (FCA), (ii) we give all the basic bricks for mining polygons with exhaustive search and pattern sampling, and (iii) we design several algorithms that we compare experimentally.
Keywords
- Machine Learning: Data Mining
- Machine Learning: Unsupervised Learning
