Multi-relational data mining

An important aspect of data mining algorithms and systems is that they should scale well to large databases. A consequence of this is that most data mining tools are based on machine learning algorithms that work on data in attribute-value format. Experience has proven that such 'single-table' mining algorithms indeed scale well. The downside of this format is, however, that more complex patterns are simply not expressible in this format and, thus, cannot be discovered. One way to enlarge the expressiveness is to generalize, as in ILP, from one-table mining to multiple table mining, i.e., to support mining on full relational databases. The key step in such a generalization is to ensure that the search space does not explode and that efficiency and, thus, scalability are maintained. In this paper we present a framework and an architecture that provide such a generalization. In this framework the semantic information in the database schema, e.g., foreign keys, are exploited to prune the search space and, in the architecture, database primitives are defined to ensure efficiency. Moreover, the framework induces a canonical generalization of algorithms, i.e., if the generalized algorithms are run on a single table database, they give the same results as their single-table counterparts. The framework is illustrated by the Warmr algorithm, which is a multi-relational generalization of the Apriori algorithm

Preference rules for label ranking: Mining patterns in multi-target relations

In this paper, we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.This research has received funding from the ECSEL Joint Undertaking, the framework programme for research and innovation horizon 2020 (2014-2020) under grant agreement number 662189-MANTIS-2014-1, and by National Funds through the FCT — Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013

Social competence at the playground: Preschoolers during recess

Social interactions at the playground have been represented as a rich learning opportunity to hone and master social skills at pre- school years. Specifically, all forms of social play (fantasy, role, ex- ercise or rough-and-tumble) have been related to children’s social competence. The main goal of this study was to examine whether it is a certain kind of social play which facilitates the development of social competence, or if it is just the opportunity for interacting during recess that provides children with an optimal environment for social learning. A total of 73 preschoolers (4–6 years old) were videotaped at the school’s playground. Teachers provided assess- ments of children’s social competence. Children’s interactions at the playground were assessed through an innovative measuring method, based on radio-frequency identification devices. The results showed a positive association between exercise play and children’s social competence. In contrast with the literature, both forms of pretend play, fantasy and role play were unrelated to children’s social competence. Smaller peer groups and longer interactions also demonstrated a positive association with these preschoolers’ social competence. The study shows the importance of outdoor physical play for preschoolers’ social success. More- over, the study suggests that the environment in which children play has an important effect on the adaptive nature of their play

Multi-interval discretization of continuous attributes for label ranking

Label Ranking (LR) problems, such as predicting rankings of financial analysts, are becoming increasingly important in data mining. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data directly. As a make-shift solution, one could consider conventional discretization methods used in classification, by simply treating each unique ranking as a separate class. In this paper, we show that such an approach has several disadvantages. As an alternative, we propose an adaptation of an existing method, MDLP, specifically for LR problems. We illustrate the advantages of the new method using synthetic data. Additionally, we present results obtained on several benchmark datasets. The results clearly indicate that the discretization is performing as expected and in some cases improves the results of the learning algorithms. © 2013 Springer-Verlag.This work was partially supported by Project Best-Case, which is co-financed by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF)

Anomaly detection in urban drainage with stereovision

This work introduces RADIUS, a framework for anomaly detection in sewer pipes using stereovision. The framework employs three-dimensional geometry reconstruction from stereo vision, followed by statistical modeling of the geometry with a generic pipe model. The framework is designed to be compatible with existing workflows for sewer pipe defect detection, as well as to provide opportunities for machine learning implementations in the future. We test the framework on 48 image sets of 26 sewer pipes in different conditions collected in the lab. Of these 48 image sets, 5 could not be properly reconstructed in three dimensions due to insufficient stereo matching. The surface fitting and anomaly detection performed well: a human-graded defect severity score had a moderate, positive Pearson correlation of 0.65 with our calculated anomaly scores, making this a promising approach to automated defect detection in urban drainage

Discovering a taste for the unusual: exceptional models for preference mining

Exceptional preferences mining (EPM) is a crossover between two subfields of data mining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where some preference relations between labels significantly deviate from the norm. It is a variant of subgroup discovery, with rankings of labels as the target concept. We employ several quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes exceptional' varies with the quality measure: two measures look for exceptional overall ranking behavior, one measure indicates whether a particular label stands out from the rest, and a fourth measure highlights subgroups with unusual pairwise label ranking behavior. We explore a few datasets and compare with existing techniques. The results confirm that the new task EPM can deliver interesting knowledge.This research has received funding from the ECSEL Joint Undertaking, the framework programme for research and innovation Horizon 2020 (2014-2020) under Grant Agreement Number 662189-MANTIS-2014-1