LIAAD - Book Chapters

Permanent URI for this collection

http://localhost:4000/handle/123456789/198

Browse

Browsing LIAAD - Book Chapters by Issue Date

Jump to a point in the index:
  • Item
    An Overview of Optimal Life Insurance Purchase, Consumption and Investment Problems
    ( 2011) Stanley R. Pliska ; Alberto Pinto ; Isabel Duarte ; Diogo Pinheiro
  • Item
    Identifying Relationships in Transactional Data
    ( 2012) João Gama ; Melissa Rodrigues ; Carlos Ferreira
    Association rules is the traditional way used to study market basket or transactional data. One drawback of this analysis is the huge number of rules generated. As a complement to Association Rules, Association Rules Network (ARN), based on Social Network Analysis (SNA) has been proposed by several researchers. In this work we study a real market basket analysis problem, available in a Belgian supermarket, using ARNs. We learn ARNs by considering the relationships between items that appear more often in the consequent of the Association Rules. Moreover, we propose a more compact variant of ARNs: the Maximal Itemsets Social Network (MISN). In order to assess the quality of these structures, we compute SNA based metrics, like weighted degree and utility of community.
  • Item
    Probabilistic ramp detection and forecasting for wind power prediction
    ( 2012) Carlos Ferreira ; Audun Botterud ; João Gama ; Vladimiro Miranda
    This paper presents a new approach to the critical problem of detecting or forecasting ramping events in the context of wind power prediction. The novelty of the model relies on departing from the probability density function estimated for the wind power and building a probabilistic representation of encountering, at each time step, a ramp event according to some definition. The model allows the assignment of a probability value to each possible magnitude of a predicted ramp and its worth is assessed by several metrics including ROC curves.
  • Item
    Bus Bunching Detection by Mining Sequences of Headway Deviations
    ( 2012) Jorge Freire ; Luís Moreira Matias ; Carlos Ferreira ; João Gama ; João Mendes Moreira
    In highly populated urban zones, it is common to notice headway deviations (HD) between pairs of buses. When these events occur in a bus stop, they often cause bus bunching (BB) in the following bus stops. Several proposals have been suggested to mitigate this problem. In this paper, we propose to find BBS (Bunching Black Spots) - sequences of bus stops where systematic HD events cause the formation of BB. We run a sequence mining algorithm, named PrefixSpan, to find interesting events available in time series. We prove that we can accurately model the BB trip usual pattern like a frequent sequence mining problem. The subsequences proved to be a promising way of identify the route' schedule points to adjust in order to mitigate such events.
  • Item
    Clustering for decision support in the fashion industry: A case study
    ( 2013) Monte,A ; Soares,C ; Pedro Brito ; Byvoet,M
    The scope of this work is the segmentation of the orders of Bivolino, a Belgian company that sells custom tailored shirts. The segmentation is done based on clustering, following a Data Mining approach. We use the K-Medoids clustering method because it is less sensitive to outliers than other methods and it can handle nominal variables, which are the most common in the data used in this work. We interpret the results from both the design and marketing perspectives. The results of this analysis contain useful knowledge for the company regarding its business. This knowledge, as well as the continued usage of clustering to support both the design and marketing processes, is expected to allow Bivolino to make important business decisions and, thus, obtain competitive advantage over its competitors. © Springer International Publishing Switzerland 2013.
  • Item
    Hop-Constrained Tree-Shaped Networks
    ( 2014) Monteiro,MSR ; Dalila Fontes ; Fontes,FACC
  • Item
    Web mining for the integration of data mining with business intelligence in web-based decision support systems
    ( 2014) Domingues,MA ; Alípio Jorge ; Carlos Manuel Soares ; Rezende,SO
    Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information sy Analysis stem that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the Web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites. © 2015, IGI Global.
  • Item
    The basic multi-project scheduling problem
    ( 2015) José Fernando Gonçalves ; De Mendes,JJM ; Resende,MGC
    In this chapter the Basic Multi-Project Scheduling Problem (BMPSP) is described, an overview of the literature on multi-project scheduling is provided, and a solution approach based on a biased random-key genetic algorithm (BRKGA) is presented. The BMPSP consists in finding a schedule for all the activities belonging to all the projects taking into account the precedence constraints and the availability of resources, while minimizing some measure of performance. The representation of the problem is based on random keys. The BRKGA generates priorities, delay times, and release dates, which are used by a heuristic decoder procedure to construct parameterized active schedules. The performance of the proposed approach is validated on a set of randomly generated problems. © Springer International Publishing Switzerland 2015.
  • Item
    A Consumption-Investment Problem with a Diminishing Basket of Goods
    ( 2015) Mousa,AS ; Pinheiro,D ; Alberto Pinto
  • Item
    An Overview of Concept Drift Applications
    ( 2016) Žliobaite,I ; Pechenizkiy,M ; João Gama
  • Item
    Federated IaaS Resource Brokerage
    ( 2016) Bruno Miguel Veloso ; Meireles,F ; Benedita Malheiro ; Burguillo,JC
  • Item
    Social Network Analysis in Streaming Call Graphs
    ( 2016) Rui Portocarrero Sarmento ; Márcia Barbosa Oliveira ; Mário Miguel Cordeiro ; Shazia Tabassum ; João Gama
  • Item
    Clustering from Data Streams
    ( 2016) João Gama
  • Item
    Online Social Networks Event Detection: A Survey
    ( 2016) Mário Miguel Cordeiro ; João Gama
    Today online social network services are challenging stateof- the-art social media mining algorithms and techniques due to its realtime nature, scale and amount of unstructured data generated. The continuous interactions between online social network participants generate streams of unbounded text content and evolutionary network structures within the social streams that make classical text mining and network analysis techniques obsolete and not suitable to deal with such new challenges. Performing event detection on online social networks is no exception, state-of-the-art algorithms rely on text mining techniques applied to pre-known datasets that are being processed with no restrictions on the computational complexity and required execution time per document analysis. Moreover, network analysis algorithms used to extract knowledge from users relations and interactions were not designed to handle evolutionary networks of such order of magnitude in terms of the number of nodes and edges. This specific problem of event detection becomes even more serious due to the real-time nature of online social networks. New or unforeseen events need to be identified and tracked on a real-time basis providing accurate results as quick as possible. It makes no sense to have an algorithm that provides detected event results a few hours after being announced by traditional newswire. © Springer International Publishing Switzerland 2016.
  • Item
    Clustering from Data Streams
    ( 2017) João Gama
  • Item
    Inductive Transfer
    ( 2017) Vilalta,Ricardo ; Carrier,ChristopheG.Giraud ; Brazdil,P ; Carlos Manuel Soares
  • Item
    Regression Trees
    ( 2017) Luís Torgo
  • Item
    Model Trees
    ( 2017) Luís Torgo
  • Item
    Professional Competence Identification Through Formal Concept Analysis
    ( 2018) Paula Raissa Silva ; Dias,SM ; Brandão,WC ; Song,MA ; Zárate,LE ; 7134
  • Item
    YAKE! Collection-Independent Automatic Keyword Extractor
    ( 2018) Ricardo Campos ; Vítor Mangaravite ; Arian Rodrigo Pasquali ; Alípio Jorge ; Nunes,C ; Jatowt,A