Towards a semantic and statistical selection of association rules

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. Association rules selection is a classical topic to address this issue, yet, new innovated approaches are required in order to provide help to decision makers. Hence, many interesting- ness measures have been defined to statistically evaluate and filter the association rules. However, these measures present two major problems. On the one hand, they do not allow eliminating irrelevant rules, on the other hand, their abun- dance leads to the heterogeneity of the evaluation results which leads to confusion in decision making. In this paper, we propose a two-winged approach to select statistically in- teresting and semantically incomparable rules. Our statis- tical selection helps discovering interesting association rules without favoring or excluding any measure. The semantic comparability helps to decide if the considered association rules are semantically related i.e comparable. The outcomes of our experiments on real datasets show promising results in terms of reduction in the number of rules

Spatial entities and cover mapping by thresholding of a vegetation index: Case of the region of Naama (Algeria)

A large variety of methods and techniques for analyzing multidate satellite images have been developed to detect changes in the Earth's surface. Based on the assumption that changes in land use are reflected in changes in radiance, the preclassificatory method was used in this study, conducted on an arid steppe region belonging to the wilaya of Nama, West Algeria. This method consists in highlighting the radiometric changes between two images of Landsat (TM, 1987) and SpotView (XS, 2007) acquired on different dates but within the same annual period.The interpretation of the two maps derived by thresholding P.V.I. (Perpenducular Vegetation Index) clearly shows the degradation of the environment. The "bare soil" and "low cover" spatial entities increased considerably in 2007 (479 and 1774 km2 respectively) compared to 1987 (258 and 1205 km2 respectively), while the other two entities "medium cover" and "dense cover" have experienced an opposite scenario at the expense of the first two. Thus, the evolution of spatial features of the vegetation cover of the study area can be perfectly monitored and the associated mapping informs very precisely about spatial changes that have occurred over time.DOI: http://doi.org/10.5281/zenodo.81003