The objective of this thesis was to investigate the possibility ofusing association rule algorithms to automatically generaterules for the output of a Parameter Tuning framework. Therules would be the basis for a recommendation to the user regardingwhich parameter space to reduce during experimentation.The parameter tuning output was generated by means ofan open source project (INPUT) example program. InPUT is atool used to describe computer experiment configurations in aframework independent input/output format. InPUT has adaptersfor the evolutionary algorithm framework Watchmakerand the tuning framework SPOT. The output was imported in Rand preprocessed to a format suitable for association rule algorithms.Experiments were conducted on data for which theparameter spaces were discretized in 2, 5, 10 steps. The minimumsupport threshold was set to 1% and 3% to investigatethe amount of rules over time. The Apriori and Eclat algorithmsproduced exactly the same amount of rules, and the top 5rules with regards to support were basically the same for bothalgorithms. It was not possible at the time to automatically distinguishinguseful rules. In combination with the many manualdecisions during the process of converting the tuning output toassociation rules, the conclusion was reached to not recommendassociation rules for enhancing the Parameter Tuningprocess.

Sensors are widely used in all aspects of our daily life including factories, hospitals and even our homes. Discovering time series association rules from sensor data can reveal the potential relationship between different sensors which can be used in many applications. However, the time series association rule mining algorithms usually produce rules much more than expected. It’s hardly to under-stand, present or make use of the rules. So we need to prune and summarize the huge amount of rules. In this paper, a two-step pruning method is proposed to reduce both the number and redundancy in the large set of time series rules. Be-sides, we put forward the BIGBAR summarizing method to summarize the rules and present the results intuitively.

This thesis focuses on mining association rules on multivariate time series. Com-mon association rule mining algorithms can usually only be applied to transactional data, and a typical application is market basket analysis. If we want to mine temporal association rules on time series data, changes need to be made. During temporal association rule mining, the temporal ordering nature of data and the temporal interval between the left and right patterns of a rule need to be considered. This thesis reviews some mining methods for temporal association rule mining, and proposes two similar algorithms for the mining of frequent patterns in single and multivariate time series. Both algorithms are scalable and efficient. In addition, temporal association rules are generated from the patterns found. Finally, the usability and efficiency of the algorithms are demonstrated by evaluating the results.

This diploma thesis deals with ways how to represent results of LISp-Miner application on the world wide web. This thesis has three main sections. The first section is dedicated to description of data analysis process including description of newly established study of infography and its application in publishing results found du-ring the DZD process. The second part describes exporting of LISp-Miner as well as exporting formats of each module and its combining with other technologies, afterwards follows summarization of all kind of exports of lispminer and its im-provements or create a whole new templates. Third part is dedicated to develop-ment of a web-based application as a tool of repsentation results generated by lispminer. The very last part is contained of future improvements of this application.

This thesis deals with data analysis using methods of knowledge discovery in databases. The goal is to select appropriate methods and tools for implementation of a specific project based on real data from Alza.cz product department. Data analysis is performed by using association rules and decision rules in the Lisp-Miner and decision trees in the RapidMiner. The methodology used is the CRISP-DM. The thesis is divided into three main sections. First section is focused on the theoretical summary of information about KDD. There are defined basic terms and described the types of tasks and methods of KDD. In the second section is introduced the methodology CRISP-DM. The practical part firstly introduces company Alza.cz and its goals for this task. Afterwards, the basic structure of the data and preparation for the next step (data mining) is described. In conclusion, the results are evaluated and the possibility of their use is outlined.

A associação é uma tarefa de mineração de dados que tem sido muito utilizada em problemas reais, porém o grande número de regras de associação que podem ser geradas dificulta a identificação de conhecimento interessante aos usuários. Para apoiar a identificação de regras interessantes podem ser utilizadas medidas de avaliação de conhecimento, que normalmente são classificadas como objetivas ou subjetivas. As medidas objetivas são mais gerais, mas podem não ser suficientes por não considerarem aspectos relacionados ao usuário ou ao domínio da aplicação. Por outro lado pode haver dificuldade em se obter a subjetividade do usuário necessária para o cálculo das medidas subjetivas. Diante desse contexto, neste trabalho é proposta uma metodologia para identificação de regras de associação interessantes que combina análises com medidas objetivas e subjetivas, visando aproveitar as vantagens de cada tipo e facilitar a participação do especialista. As medidas objetivas são utilizadas para selecionar algumas regras potencialmente interessantes para serem avaliadas por um especialista. As medidas subjetivas são calculadas utilizando essas regras com as avaliações do especialista. Essas medidas subjetivas então são utilizadas para auxiliar a identificação de regras interessantes de acordo com o conhecimento obtido durante a avaliação. Para viabilizar a aplicação dessa metodologia foi desenvolvido um módulo computacional de exploração de regras de associação com medidas subjetivas, denominado RulEE-SEAR. Utilizando esse módulo e outras ferramentas já existentes foi realizado um estudo de caso com uma base de dados real sobre qualidade de vida urbana. Nesse estudo de caso o processo de identificação de regras de associação interessantes foi realizado com especialista da área e verificou-se a viabilidade da metodologia proposta. / Association is a data mining task which has been applied in several real problems. However, due to the huge number of association rules that can be generated, it is hard for users to identify interesting knowledge. To assist users in finding interesting rules, evaluation measures can be used. Those measures are usually divided into objective and subjective. Objective measures are more general, but they can be insufficient because they do not consider user's and domain's features. On the other hand, getting users's knowledge and interest needed to calculate subjective measures can be a difficult task. In this context, a methodology to identify interesting association rules is proposed in this work. This methodology combines analysis with objective and subjective measures, aiming to use the advantages of each kind of measure and to make user's participation easier. Objective measures are used to select some potentially interesting rules for the user's evaluation. These rules and the evaluation are used to calculate subjective measures. Then, the subjective measures are used to assist the user in identifying interesting rules according to the knowledge obtained during the evaluation. To make the methodology use practicable, a computational module, named RulEE-SEAR, was developed to explore the association rules with subjective measures. Using this module and other existing tools, a case study was done. A urban life quality database was used and a specialist in this area participated in the interesting association rules identification. That case study showed that the methodology proposed is feasible.

A demanda por métodos de análise e descoberta de conhecimento em grandes bases de dados tem fortalecido a pesquisa em Mineração de Dados. Dentre as tarefas associadas a essa área, tem-se Regras de Associação. Vários algoritmos foram propostos para tratamento de Regras de Associação, que geralmente tˆem como resultado um elevado número de regras, tornando o Pós-processamento do conhecimento uma etapa bastante complexa e desafiadora. Existem medidas para auxiliar essa etapa de avaliação de regras, porém existem lacunas referentes a inexistência de um método intuitivo para priorizar e selecionar regras. Além disso, não é possível encontrar metodologias específicas para seleção de regras considerando mais de uma medida simultaneamente. Esta tese tem como objetivo a proposição, desenvolvimento e implementação de uma metodologia para o Pós-processamento de Regras de Associação. Na metodologia proposta, pequenos grupos de regras identificados como potencialmente interessantes são apresentados ao usuário especialista para avaliação. Para tanto, foram analisados métodos e técnicas utilizadas em Pós-processamento de conhecimento, medidas objetivas para avaliação de Regras de Associação e algoritmos que geram regras. Dessa perspectiva foram realizados experimentos para identificar o potencial das medidas a serem empregadas como filtros de Regras de Associação. Uma avaliação gráfica apoiou o estudo das medidas e a especificação da metodologia proposta. Aspecto inovador da metodologia proposta é a utilização do método de Pareto e a combinação de medidas para selecionar as Regras de Associação. Por fim foi implementado um ambiente para avaliação de Regras de Associação, denominado ARInE, viabilizando o uso da metodologia proposta. / The large demand of methods for knowledge discovery and analysis in large databases has continously increased the research in data mining area. Among the tasks associated to this area, one can find Association Rules. Several algorithms have been proposed for treating Association Rules. However, these algorithms give as results a huge amount of rules, making the knowledge post-processing phase very complex and challeging. There are several measures that can be used in this evaluation phase, but there are also some limitations regarding to the ausence of an intuitive method to rank and select rules. Moreover, it is not possible to find especific methodologies for selecting rules, considering more than one measure simultaneously. This thesis has as objective the proposal, development and implementation of a postprocessing methodology for Association Rules. In the proposed methodology, small groups of rules, which have been identified as potentialy interesting, are presented to the expert for evaluation. In this sense, methods and techniques for knowledge post-processing, objective measures for rules evaluation, and Association Rules algorithms have been analized. From this point of view, several experiments have been realized for identifying the potential of such measures to be used to filter Association Rules. The study of measures and the specification of the proposed methodology have been supported by a graphical evaluation. The novel aspect of the proposed methodology consists on using the Pareto’s method and combining measures for selecting Association Rules. Finally, an enviroment for evaluating Association Rules, named as ARInE, has been implemented according to the proposed methodology.

A alta disponibilidade de informações em saúde por meio de sistemas de informação só pode ser proporcionada com a utilização de sistemas que sejam capazes de trocar dados de forma segura e consistente. Para isso, estes sistemas necessitam ser interoperáveis, capazes de trocar informações. Uma das características mais importantes de tais sistemas é a utilização de terminologias em saúde, permitindo a codificação dos termos clínicos de maneira robusta e consistente. Algumas das terminologias mais conhecidas e utilizadas são: SNOMED-CT, ICD-CM, ICD, LOINC, NANDA, TUSS, CBHPM, Tabela de Procedimentos SUS, entre outras. Quando os sistemas não se utilizam de uma mesma terminologia para codificação de um mesmo conceito é necessário a realização de mapeamentos e traduções entre as terminologias. O mapeamento entre terminologias consiste em estabelecer as associações pertinentes às terminologias para que cada termo pertencente a uma possa ser associado a algum termo da outra. Este mapeamento, geralmente, é criado por especialistas de domínio, que atuam analisando as duas terminologias em questão e estabelecendo manualmente estas associações. Neste trabalho, propomos uma metodologia que visa facilitar a realização deste tipo de mapeamento, por meio da utilização de dois recursos: Regras de Associação, para extração das associações preexistentes entre as terminologias em registros clínicos; e Busca Textual, para pareamento entre conceitos das duas terminologias baseado na identificação de termos comuns. O auxílio à criação destes mapeamentos é proporcionado por meio de sugestões de relações existentes entre as terminologias. Como resultado deste trabalho obtivemos uma metodologia genérica de mapeamento entre terminologias capaz de auxiliar com sucesso os especialistas. Em aproximadamente 40% dos casos os especialistas concordaram com uma das sugestões apresentadas. De forma complementar, obtivemos o mapeamento parcial entre duas terminologias: a ICD9-CM e a TUSS, utilizadas como caso de uso para validação da metodologia. / The high availability of health information through information systems can be provided only with the use of systems that are able to exchange data securely and consistently. To this end, these systems need to be interoperable, capable of exchanging information that is understood both at one end as the other. One of the most important characteristics of such systems is the use of terminologies in health, allowing the coding of clinical terms in a robust and consistent manner. Some of the most known and used terminologies are: SNOMED-CT, ICD-CM, ICD, LOINC, NANDA, TUSS, CBHPM, and SUS Procedures Table, among others. When systems do not use the same terminology for encoding the same concept, it is necessary to perform mappings and translations between the terminologies. The mapping between terminologies consists on establishing the relevant associations present in terminologies, so that each term belonging to one can be associated unambiguously to the terms belonging to another. This mapping is typically created by domain experts who work analyzing the two terms in question and manually setting these associations. In this paper, we propose a methodology that aims to facilitate this type of mapping, through the use of two frameworks: Association Rules, for the extraction of preexisting associations between the terminologies in clinical records; and Textual Search, for pairing between the two terminologies concepts based on the identification of common terms. The creation of these mappings by experts is aided by the method suggesting links between the terminologies through the Association Rules or Textual Search. As a result of this work we obtained a generic methodology for mapping between terminologies able to successfully assist the experts. In approximately 40% of cases the experts agreed with the suggestions. As a complement, we obtained a partial mapping between two specific terminologies for coding surgical procedures: the ICD9-CM and TUSS, used as use case to validate the methodology.

Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients¿ records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients¿ personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making.

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