Abstract

Abstract
Relatively simple transformations can speed up the execution of queries for data mining considerably. While some ILP systems use such transformations, relatively little is known about them or how they relate to each other. This paper describes a number of such transformations. Not all of them are novel, but there have been no studies comparing their efficacy. The main contributions of the paper are: (a) it clarifies the relationship between the transformations; (b) it contains an empirical study of what can be gained by applying the transformations; and (c) it provides some guidance on the kinds of problems that are likely to benefit from the transformations.

Abstract

Abstract
One of the most well known successes of Inductive Logic Programming (ILP) is on Structure-Activity Relationship (SAR) problems. In such problems, ILP has proved several times to be capable of constructing expert comprehensible models that hell) to explain the activity of chemical compounds based on their structure and properties. However, despite its successes on SAR problems, ILP has severe scalability problems that prevent its application oil larger datasets. In this paper we present LogCHEM, an ILP based tool for discriminative interactive mining of chemical fragments. LogCHEM tackles ILP's scalability issues in the context of SAR applications. We show that LogCHEM benefits from the flexibility of ILP both by its ability to quickly extend the original mining model, and by its ability, to interface with external tools. Furthermore, We demonstrate that LogCHEM can be used to mine effectively large chemoinformatics datasets, namely, several datasets from EPA's DSSTox database and on a dataset based on the DTP AIDS anti-viral screen.

Abstract
The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3D-structure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely alpha-helices and beta-sheets, which are intermediate levels of the local structure.

Abstract
Wildfires can importantly affect the ecology and economy of large regions of the world. Effective prevention techniques are fundamental to mitigate their consequences. The design of such preemptive methods requires a deep understanding of the factors that increase the risk of fire, particularly when we can intervene on these factors. This is the case for the maintenance of ecological balances in the landscape that minimize the occurrence of wildfires. We use an inductive logic programming approach over detailed spatial datasets: one describing the landscape mosaic and characterizing it in terms of its use; and another describing polygonal areas where wildfires took place over several years. Our inductive process operates over a logic term representation of vectorial geographic data and uses spatial predicates to explore the search space, leveraging the framework of Spatial-Yap, its multi-dimensional indexing and tabling extensions. We show that the coupling of a logic-based spatial database with an inductive logic programming engine provides an elegant and powerful approach to spatial data mining.