Η παρούσα διατριβή ασχολήθηκε με την εφαρμογή τεχνικών data mining σε συστήματα ηλεκτρονικού εμπορίου. Για να είμαστε πιο ακριβείς επικεντρωθήκαμε στην εύρεση κανόνων συσχετίσεων από δεδομένα, και κύρια δεδομένα που είχαν να κάνουν με βάσεις συναλλαγών. Η βασική ιδέα ενός κανόνα συσχετίσεως είναι να αναπτύξει μια συστηματική μέθοδο με την οποία ένας χρήστης μπορεί να προβλέψει την εμφάνιση κάποιων αντικειμένων, δοσμένης της ύπαρξης κάποιων άλλων σε μια συναλλαγή, και συνήθως αποτελούν συνεπαγωγές της μορφής Χ=>Y. Παράδειγμα ενός τέτοιου κανόνα είναι: “οι πελάτες που αγοράζουν κινητά τηλέφωνα και handsfree αγοράζουν και θήκη για το κινητό τους”. Τα τελευταία χρόνια είχε γίνει κοινός τόπος όλων των μελετών και των ερευνητών οι αδυναμίες και τα μειονεκτήματα του μοντέλου εύρεσης κανόνων συσχετίσεων. Στόχος μας ήταν να επιλύσουμε υπάρχοντα προβλήματα αλλά και να εκθέσουμε και να αντιμετωπίσουμε κάποια νέα. Σαν σύγγραμμα η παρούσα διατριβή μπορεί να χωριστεί σε τρία κομμάτια. Το πρώτο είναι τα τρία πρώτα κεφάλαια, τα οποία και αποτελούν εισαγωγικά κεφάλαια απαραίτητα για την υποστήριξη και κατανόηση της δουλειάς μας. Ακολούθως τα κεφάλαια 4 έως 8 αποτελούν το δεύτερο και κύριο κομμάτι της παρούσας διατριβής, και περιγράφουν διάφορες τεχνικές και προτάσεις μας, αποτελέσματα της ερευνάς μας. Το τρίτο και τελευταίο κομμάτι της διατριβής, αναφορικά το Κεφάλαιο 9, αποτελεί την σύνοψη ολόκληρης της εργασίας μας όπου παραθέτουμε εν συντομία την τελική προσφορά μας στο χώρο, δίνουμε πιθανές εφαρμογές των προτάσεων μας, και τέλος προτείνουμε μελλοντικές κατευθύνσεις της έρευνας σε ανοιχτά πεδία – προβλήματα. / -

The advance of information technologies (IT) makes it possible to collect a massive amount of data in business applications and information systems. The increasing data volumes require more effective knowledge discovery techniques to make the best use of the data. This dissertation focuses on knowledge discovery on graph-structured data, i.e., graph-based learning. Graph-structured data refers to data instances with relational information indicating their interactions in this study. Graph-structured data exist in a variety of application areas related to information systems, such as business intelligence, knowledge management, e-commerce, medical informatics, etc. Developing knowledge discovery techniques on graph-structured data is critical to decision making and the reuse of knowledge in business applications.In this dissertation, I propose a graph-based learning framework and identify four major knowledge discovery tasks using graph-structured data: topology description, node classification, link prediction, and community detection. I present a series of studies to illustrate the knowledge discovery tasks and propose solutions for these example applications. As to the topology description task, in Chapter 2 I examine the global characteristics of relations extracted from documents. Such relations are extracted using different information processing techniques and aggregated to different analytical unit levels. As to the node classification task, Chapter 3 and Chapter 4 study the patent classification problem and the gene function prediction problem, respectively. In Chapter 3, I model knowledge diffusion and evolution with patent citation networks for patent classification. In Chapter 4, I extend the context assumption in previous research and model context graphs in gene interaction networks for gene function prediction. As to the link prediction task, Chapter 5 presents an example application in recommendation systems. I frame the recommendation problem as link prediction on user-item interaction graphs, and propose capturing graph-related features to tackle this problem. Chapter 6 examines the community detection task in the context of online interactions. In this study, I propose to take advantage of the sentiments (agreements and disagreements) expressed in users' interactions to improve community detection effectiveness. All these examples show that the graph representation allows the graph structure and node/link information to be more effectively utilized in addressing the four knowledge discovery tasks.In general, the graph-based learning framework contributes to the domain of information systems by categorizing related knowledge discovery tasks, promoting the further use of the graph representation, and suggesting approaches for knowledge discovery on graph-structured data. In practice, the proposed graph-based learning framework can be used to develop a variety of IT artifacts that address critical problems in business applications.

Given the vast amount of online information covering almost all aspects of human endeavor, the Internet, especially the Web, is clearly a fertile ground for data mining research from which to extract valuable knowledge. Web mining is the application of data mining techniques to extract knowledge from Web data, including Web documents, Web hyperlink structure, and Web usage logs.Traditional Web mining research has been mainly focused on addressing the information overload problem. Many information retrieval (IR) and artificial intelligence (AI) techniques have been adopted or developed to identify relevant information from the Web to meet users' specific information needs. However, most existing studies do not fully explore the social and behavioral aspects of the Web. Thus, the primary goal of this dissertation is to develop an integrated research framework that extends traditional Web mining methodologies to fully explore the technical, social, and behavioral aspects of Web knowledge discovery.My dissertation framework is composed of technical and social/behavioral components. In the technical component of my dissertation, a set of domain specific Web collection building, Web content and link structure mining, and Web knowledge presentation techniques were developed. These techniques were tested in a series of case studies to demonstrate their effectiveness and efficiency in facilitating knowledge discovery in various domains.The social/behavioral component of my dissertation is to explore the application of Web mining technology as a new means to study the social interactions and behavior of Web content providers and users. Several case studies were conducted to extract knowledge on covert organizations' resource allocation plans, information management policies, and technical sophistication using Web mining techniques. Such knowledge would be very difficult to obtain through other means.The major contributions of this dissertation are twofold. First, it proposed a set of new Web mining techniques that can help facilitate knowledge discovery in various domains. Second, it demonstrated the effectiveness and efficiency of applying Web mining techniques in extracting social and behavioral knowledge in different contexts.

Given that data is increasing exponentially everyday, extracting and understanding the information, themes and relationships from large collections of documents is more and more important to researchers in many areas. In this paper, we present a cooperative semantic information processing system to help biomedical researchers understand and discover knowledge in large numbers of titles and abstracts from PubMed query results. Our system is based on a prevalent technique, topic modeling, which is an unsupervised machine learning approach for discovering the set of semantic themes in a large set of documents. In addition, we apply a natural language processing technique to transform the “bag-of-words” assumption of topic models to the “bag-of-important-phrases” assumption and build an interactive visualization tool using a modified, open-source, Topic Browser. In the end, we conduct two experiments to evaluate the approach. The first, evaluates whether the “bag-of-important-phrases” approach is better at identifying semantic themes than the standard “bag-of-words” approach. This is an empirical study in which human subjects evaluate the quality of the resulting topics using a standard “word intrusion test” to determine whether subjects can identify a word (or phrase) that does not belong in the topic. The second is a qualitative empirical study to evaluate how well the system helps biomedical researchers explore a set of documents to discover previously hidden semantic themes and connections. The methodology for this study has been successfully used to evaluate other knowledge-discovery tools in biomedicine.

Abstract With the phenomenal growth of electronic data and information, there are many demands for the development of efficient and effective systems (tools) to perform the issue of data mining tasks on multidimensional databases. Association rules describe associations between items in the same transactions (intra) or in different transactions (inter). Association mining attempts to find interesting or useful association rules in databases: this is the crucial issue for the application of data mining in the real world. Association mining can be used in many application areas, such as the discovery of associations between customers’ locations and shopping behaviours in market basket analysis. Association mining includes two phases. The first phase, called pattern mining, is the discovery of frequent patterns. The second phase, called rule generation, is the discovery of interesting and useful association rules in the discovered patterns. The first phase, however, often takes a long time to find all frequent patterns; these also include much noise. The second phase is also a time consuming activity that can generate many redundant rules. To improve the quality of association mining in databases, this thesis provides an alternative technique, granule-based association mining, for knowledge discovery in databases, where a granule refers to a predicate that describes common features of a group of transactions. The new technique first transfers transaction databases into basic decision tables, then uses multi-tier structures to integrate pattern mining and rule generation in one phase for both intra and inter transaction association rule mining. To evaluate the proposed new technique, this research defines the concept of meaningless rules by considering the co-relations between data-dimensions for intratransaction-association rule mining. It also uses precision to evaluate the effectiveness of intertransaction association rules. The experimental results show that the proposed technique is promising.

Made available in DSpace on 2014-06-11T19:30:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2007-02-26Bitstream added on 2014-06-13T21:01:19Z : No. of bitstreams: 1 franco_c_dr_prud.pdf: 7388592 bytes, checksum: 3b1b05541970e72cb28df9a6b857ffe8 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Universidade Estadual de Maringa - Uem / Nas últimas décadas, a capacidade de gerar e coletar dados aumentou rapidamente, gerando a necessidade do desenvolvimento de novas técnicas e ferramentas capazes de processar e analisar esses dados descobrindo informações novas e úteis. Surgindo um proeminente campo de pesquisa para a extração de conhecimento de dados Descoberta de Conhecimento em Banco de Dados. Pela aplicação da metodologia da descoberta de conhecimento indireto aos atributos dos fragmentos cerâmicos coletados ao nível do solo no Sítio Arqueológico Piracanjuba Piraju SP, este trabalho pretende prover aos peritos em arqueologia uma visão sistêmica capaz de auxiliá-los no conhecimento das populações pretéritas que ali habitaram. / In the last decades, the capacities to produce and collect data has grown fast and the development of news techniques and tools capable to processes and analyze this datas discovering new and useful information as necessary. Therefore, a huge research area has beginning for the extraction of data understanding Knowledge Discovery in Database. The indirect knowledge discovery applied to ceramic fragment collected at soil level in Piracanjuba's Piraju, SP aims give to archaeology experts a whole vision able to be useful knowledge of the past people living there.

Resumo: Nas últimas décadas, a capacidade de gerar e coletar dados aumentou rapidamente, gerando a necessidade do desenvolvimento de novas técnicas e ferramentas capazes de processar e analisar esses dados descobrindo informações novas e úteis. Surgindo um proeminente campo de pesquisa para a extração de conhecimento de dados Descoberta de Conhecimento em Banco de Dados. Pela aplicação da metodologia da descoberta de conhecimento indireto aos atributos dos fragmentos cerâmicos coletados ao nível do solo no Sítio Arqueológico Piracanjuba Piraju SP, este trabalho pretende prover aos peritos em arqueologia uma visão sistêmica capaz de auxiliá-los no conhecimento das populações pretéritas que ali habitaram. / Abstract: In the last decades, the capacities to produce and collect data has grown fast and the development of news techniques and tools capable to processes and analyze this datas discovering new and useful information as necessary. Therefore, a huge research area has beginning for the extraction of data understanding Knowledge Discovery in Database. The indirect knowledge discovery applied to ceramic fragment collected at soil level in Piracanjuba's Piraju, SP aims give to archaeology experts a whole vision able to be useful knowledge of the past people living there. / Orientador: Nilton Nobuhiro Imai / Coorientador: Neide Faccio Barrocá / Coorientador: Vilma Tachibana / Banca: Milton Hirokazu Shimabukuro / Banca: Mário Hissamitsu Tarumoto / Banca: José Luiz de Morais / Banca: Emília Mariko Kashimoto / Doutor

Today, the importance of big data from lifestyles and work activities has been the focus of much research. At the same time, advances in modern sensor technologies have enabled self-logging of a signi cant number of daily activities and movements. Lifestyle logging produces a wide variety of personal data along the lifespan of individuals, including locations, movements, travel distance, step counts and the like, and can be useful in many areas such as healthcare, personal life management, memory recall, and socialisation. However, the amount of obtainable personal life logging data has enormously increased and stands in need of effective processing, analysis, and visualisation to provide hidden insights owing to the lack of semantic information (particularly in spatiotemporal data), complexity, large volume of trivial records, and absence of effective information visualisation on a large scale. Meanwhile, new technologies such as visual analytics have emerged with great potential in data mining and visualisation to overcome the challenges in handling such data and to support individuals in many aspects of their life. Thus, this thesis contemplates the importance of scalability and conducts a comprehensive investigation into visual analytics and its impact on the process of knowledge discovery from the European Commission project MyHealthAvatar at the Centre for Visualisation and Data Analytics by actively involving individuals in order to establish a credible reasoning and effectual interactive visualisation of such multivariate data with particular focus on lifestyle and personal events. To this end, this work widely reviews the foremost existing work on data mining (with the particular focus on semantic enrichment and ranking), data visualisation (of time-oriented, personal, and spatiotemporal data), and methodical evaluations of such approaches. Subsequently, a novel automated place annotation is introduced with multilevel probabilistic latent semantic analysis to automatically attach relevant information to the collected personal spatiotemporal data with low or no semantic information in order to address the inadequate information, which is essential for the process of knowledge discovery. Correspondingly, a multi-signi ficance event ranking model is introduced by involving a number of factors as well as individuals' preferences, which can influence the result within the process of analysis towards credible and high-quality knowledge discovery. The data mining models are assessed in terms of accurateness and performance. The results showed that both models are highly capable of enriching the raw data and providing significant events based on user preferences. An interactive visualisation is also designed and implemented including a set of novel visual components signifi cantly based upon human perception and attentiveness to visualise the extracted knowledge. Each visual component is evaluated iteratively based on usability and perceptibility in order to enhance the visualisation towards reaching the goal of this thesis. Lastly, three integrated visual analytics tools (platforms) are designed and implemented in order to demonstrate how the data mining models and interactive visualisation can be exploited to support different aspects of personal life, such as lifestyle, life pattern, and memory recall (reminiscence). The result of the evaluation for the three integrated visual analytics tools showed that this visual analytics approach can deliver a remarkable experience in gaining knowledge and supporting the users' life in certain aspects.

O crescimento do uso de sistemas de posicionamento global (GPS) e outros sistemas de localização espacial tornaram possível o rastreamento de objetos móveis, produzindo um grande volume de um novo tipo de dado, chamado trajetórias de objetos móveis. Existe, entretanto, uma forte lacuna entre a quantidade de dados extraídos destes dispositivos, dotados de sistemas GPS, e a descoberta de conhecimento que se pode inferir com estes dados. Um tipo de descoberta de conhecimento em dados de trajetórias de objetos móveis é a classificação. A classificação de trajetórias é um tema de pesquisa relativamente novo, e poucos métodos tem sido propostos até o presente momento. A maioria destes métodos foi desenvolvido para uma aplicação específica. Poucos propuseram um método mais geral, aplicável a vários domínios ou conjuntos de dados. Este trabalho apresenta um novo método de classificação que transforma as trajetórias em séries temporais, de forma a obter características mais discriminativas para a classificação. Experimentos com dados reais mostraram que o método proposto é melhor do que abordagens existentes. / The growing use of global positioning systems (GPS) and other location systems made the tracking of moving objects possible, producing a large volume of a new kind of data, called trajectories of moving objects. However, there is a large gap between the amount of data generated by these devices and the knowledge that can be inferred from these data. One type of knowledge discovery in trajectories of moving objects is classification. Trajectory classification is a relatively new research subject, and a few methods have been proposed so far. Most of these methods were developed for a specific application. Only a few have proposed a general method, applicable to multiple domains or datasets. This work presents a new classification method that transforms the trajectories into time series, in order to obtain more discriminative features for classification. Experiments with real trajectory data revealed that the proposed approach is more effective than existing approaches.

As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol. / Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.