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Schmidt F.,ISC Gebhardt | Spott M.,BT Innovate and Design
Advances in Intelligent Systems and Computing | Year: 2013

In the past years pattern detection has gained in importance for many companies. As the volume of collected data increases so does typically the number of found patterns. To cope with this problem different interestingness measures for patterns have been proposed. Unfortunately, their usefulness turns out to be limited in practical applications. To address this problem, we propose a novel visualisation technique that allows analysts to explore patterns interactively rather than presenting analysts with static ordered lists of patterns. Specifically, we focus on an interactive visualisation of temporal frequent item sets with hierarchical attributes. © 2013 Springer-Verlag.

Tandur D.,Agilent Technologies | Duplicy J.,Agilent Technologies | Arshad K.,University of Greenwich | Depierre D.,Thales Alenia | And 5 more authors.
IEEE Vehicular Technology Magazine | Year: 2012

A vertically integrated approach is presented to evaluate the performance of cognitive radio (CR) systems. The approach consists of three pillars: measurement, modeling, and emulation (MME). This integrated approach enables the reproduction of the radio environment in laboratory conditions and aims to guarantee the same performance results as one would obtain in the field. This article provides a detailed explanation for each pillar along with state-of-the-art overviews. Finally, a test bed based on the MME approach is presented. © 2012 IEEE.

Shakya S.,BT Innovate and Design | Santana R.,Technical University of Madrid
Adaptation, Learning, and Optimization | Year: 2012

In this chapter we describe Markovian Optimisation Algorithm (MOA), one of the recent developments in MN based EDA. It uses the local Markov property to model the dependency and directly sample from it without needing to approximate a complex join probability distribution model. MOA has a much simpler workflow in comparison to its global property based counter parts, since expensive processes to finding cliques, and building and estimating clique potential functions are avoided. The chapter is intended as an introductory chapter, and describes the motivation and the workflow of MOA. It also reviews some of the results obtained with it. © Springer-Verlag Berlin Heidelberg 2012.

Santana R.,University of the Basque Country | Shakya S.,BT Innovate and Design
Adaptation, Learning, and Optimization | Year: 2012

This chapter introduces probabilistic graphical models and explain their use for modelling probabilistic relationships between variables in the context of optimisation with EDAs.We focus on Markov networksmodels and review different algorithms used to learn and sample Markov networks. Other probabilistic graphical models are also reviewed and their differences with Markov networks are analysed. © Springer-Verlag Berlin Heidelberg 2012.

Shakya S.,BT Innovate and Design | Santana R.,Technical University of Madrid
Adaptation, Learning, and Optimization | Year: 2012

This chapter reviews some of the popular EDAs based on Markov Networks. It starts by giving introduction to general EDAs and describes the motivation behind their emergence. It then categorises EDAs according to the type of probabilistic models they use (directed model based, undirected model based and common model based) and briefly lists some of the popular EDAs in each categories. It then further focuses on undirected model based EDAs, describes their general workflow and the history, and briefly reviews some of the popular EDAs based on undirected models. It also outlines some of the current research work in this area. © Springer-Verlag Berlin Heidelberg 2012.

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