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Publicações

Publicações por Paulo Jorge Azevedo

2008

A methodology for exploring association models

Autores
Jorge, A; Pocas, J; Azevedo, PJ;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Visualization in data mining is typically related to data exploration. In this chapter we present a methodology for the post processing and visualization of association rule models. One aim is to provide the user with a tool that enables the exploration of a large set of association rules. The method is inspired by the hypertext metaphor. The initial set of rules is dynamically divided into small comprehensible sets or pages, according to the interest of the user. From each set, the user can move to other sets by choosing one appropriate operator. The set of available operators transform sets of rules into sets of rules, allowing focusing on interesting regions of the rule space. Each set of rules can also be then seen with different graphical representations. The tool is web-based and dynamically generates SVG pages to represent graphics. Association rules are given in PMML format. © 2008 Springer-Verlag Berlin Heidelberg.

2005

An experiment with association rules and classification: Post-bagging and conviction

Autores
Jorge, AM; Azevedo, PJ;

Publicação
DISCOVERY SCIENCE, PROCEEDINGS

Abstract
In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy. We also discuss the predictive power of different metrics used for association rule mining, such as confidence, lift, conviction and chi(2). We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction.

2004

Model-based collaborative filtering for team building support

Autores
Veloso, M; Jorge, A; Azevedo, PJ;

Publicação
ICEIS 2004 - Proceedings of the Sixth International Conference on Enterprise Information Systems

Abstract
In this paper we describe an application of recommender systems to team building in a company or organization. The recommender system uses a collaborative filtering model based approach. Recommender models are sets of association rules extracted from the activity log of employees assigned to projects or tasks. Recommendation is performed at two levels: first by recommending a single team element given a partially built team; and second by recommending changes to a completed team. The methodology is applied to a case study with real data. The results are evaluated through experimental tests and one survey to potential users.

2023

Social network analytics and visualization: Dynamic topic-based influence analysis in evolving micro-blogs

Autores
Tabassum, S; Gama, J; Azevedo, PJ; Cordeiro, M; Martins, C; Martins, A;

Publicação
EXPERT SYSTEMS

Abstract
Influence Analysis is one of the well-known areas of Social Network Analysis. However, discovering influencers from micro-blog networks based on topics has gained recent popularity due to its specificity. Besides, these data networks are massive, continuous and evolving. Therefore, to address the above challenges we propose a dynamic framework for topic modelling and identifying influencers in the same process. It incorporates dynamic sampling, community detection and network statistics over graph data stream from a social media activity management application. Further, we compare the graph measures against each other empirically and observe that there is no evidence of correlation between the sets of users having large number of friends and the users whose posts achieve high acceptance (i.e., highly liked, commented and shared posts). Therefore, we propose a novel approach that incorporates a user's reachability and also acceptability by other users. Consequently, we improve on graph metrics by including a dynamic acceptance score (integrating content quality with network structure) for ranking influencers in micro-blogs. Additionally, we analysed the topic clusters' structure and quality with empirical experiments and visualization.

2019

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

Autores
de Sá, CR; Azevedo, PJ; Soares, C; Jorge, AM; Knobbe, AJ;

Publicação
CoRR

Abstract

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