Mining and Filtering Multi-level Spatial Association Rules with ARES

In spatial data mining, a common task is the discovery of spatial association rules from spatial databases. We propose a distributed system, named ARES that takes advantage of the use of a multi-relational approach to mine spatial association rules. It supports spatial database coupling and discovery of multi-level spatial association rules as a means for spatial data exploration. We also present some criteria to bias the search and to filter the discovered rules according to user's expectations. Finally, we show the applicability of our proposal to two different real world domains, namely, document image processing and geo-referenced analysis of census data

Personality Traits in Miners with Past Occupational Elemental Mercury Exposure

In this study, we evaluated the impact of long-term occupational exposure to elemental mercury vapor (Hg(0)) on the personality traits of ex-mercury miners. Study groups included 53 ex-miners previously exposed to Hg(0) and 53 age-matched controls. Miners and controls completed the self-reporting Eysenck Personality Questionnaire and the Emotional States Questionnaire. The relationship between the indices of past occupational exposure and the observed personality traits was evaluated using Pearson’s correlation coefficient and on a subgroup level by machine learning methods (regression trees). The ex-mercury miners were intermittently exposed to Hg(0) for a period of 7–31 years. The means of exposure-cycle urine mercury (U-Hg) concentrations ranged from 20 to 120 μg/L. The results obtained indicate that ex-miners tend to be more introverted and sincere, more depressive, more rigid in expressing their emotions and are likely to have more negative self-concepts than controls, but no correlations were found with the indices of past occupational exposure. Despite certain limitations, results obtained by the regression tree suggest that higher alcohol consumption per se and long-term intermittent, moderate exposure to Hg(0) (exposure cycle mean U-Hg concentrations > 38.7 < 53.5 μg/L) in interaction with alcohol remain a plausible explanation for the depression associated with negative self-concept found in subgroups of ex-mercury miners. This could be one of the reason for the higher risk of suicide among miners of the Idrija Mercury Mine in the last 45 years

Evidential Bagging: Combining Heterogeneous Classifiers in the Belief Functions Framework

International audienceIn machine learning, Ensemble Learning methodologies are known to improve predictive accuracy and robustness. They consist in the learning of many classifiers that produce outputs which are finally combined according to different techniques. Bagging, or Bootstrap Aggre-gating, is one of the most famous Ensemble methodologies and is usually applied to the same classification base algorithm, i.e. the same type of classifier is learnt multiple times on bootstrapped versions of the initial learning dataset. In this paper, we propose a bagging methodology that involves different types of classifier. Classifiers' probabilist outputs are used to build mass functions which are further combined within the belief functions framework. Three different ways of building mass functions are proposed; preliminary experiments on benchmark datasets showing the relevancy of the approach are presented

Understanding Group Structures and Properties in Social Media

Abstract. The rapid growth of social networking sites enables people to connect to each other more conveniently than ever. With easy-to-use social media, people contribute and consume contents, leading to a new form of human interaction and the emergence of online collective behav-ior. In this chapter, we aim to understand group structures and proper-ties by extracting and profiling communities in social media. We present some challenges of community detection in social media. A prominent one is that networks in social media are often heterogeneous. We intro-duce two types of heterogeneity presented in online social networks and elaborate corresponding community detection approaches for each type, respectively. Social media provides not only interaction information but also textual and tag data. This variety of data can be exploited to profile individual groups in understanding group formation and relationships. We also suggest some future work in understanding group structures and properties. Key words: social media, community detection, group profiling, het-erogeneous networks, multi-mode networks, multi-dimensional networks