Graph Patterns

Pattern mining usually results in huge amounts of patterns, among which only small percentages are interesting. In this paper, Surprisingness (including Surpringness_I and Surpringness_II) is proposed as an innovative objective multivariate interestingness measure for automatically identifying interesting patterns from a large quantity of patterns. Surprisingness is applicable in unstructured or semi-structured, multi-domain or mixed-domain data compared to existing measures. An experiment has been conducted enabling unsupervised learning of common sense, interesting patterns and exceptions from a knowledge graph database built from Wikipedia 1 extracted data (represented as directed labeled hypergraphs), using Surpringness.

Diseases in Agricultural Production Systems represent one of the biggest drivers of losses and poor quality products. In the case of coffee production, experts in this area believe that weather conditions, along with physical properties of the crop are the main variables that determine the development of a disease known as Coffee Rust. On the other hand, several Artificial Intelligence techniques allow the analysis of agricultural environment variables in order to obtain their relationship with specific problems, such as diseases in crops. In this paper an extraction of rules to detect rust in coffee from induction of decision trees and expert knowledge is addressed. Finally, a graph-based representation of these rules is submitted, in order to obtain a model with greater expressiveness and interpretability.