This paper is focusing on the data mining solutions for the WWW, specifically how it can be used for the hyperlinks evaluation. We are focusing on the hyperlinks used in the web sites systems and on the problem which consider evaluation of its utility. Since hyperlinks reflect relation to other webpage one can expect that there exist way to verify if users follow desired navigation paths. The Challenge is to use available techniques to discover usage behavior patterns and interpret them. We have evaluated hyperlinks of the selected pages from www.bth.se web site. By using web expert’s help the usefulness of the data mining as the assessment basis was validated. The outcome of the research shows that data mining gives decision support for the changes in the web site navigational structure. / akasha.kate@gmail.com

Postprocessing is like data preparation one of the most challenging tasks in data mining that users must deal with. It is desirable to simplify it so that the path to results is as fast and efficient as possible. The extension of the EasyMiner research project to filter the association rules by similarity to the knowledge-based rules, should be helped in this respect, which is the subject of this diploma thesis. The objectives were accomplished by a detailed analysis of the default state of EasyMiner in conjunction with a thoroughly thought-out implementation proposal without increasing the demands on the server or user of the application. An analysis of general practices and the author's deep knowledge of Internet application issues served to do this. The future deployment of this extension to the EasyMiner infrastructure will benefit from a clearer and more efficient work with the Knowledge base part, where it will no longer be necessary to evaluate the interest in the same or similar rules and the user will be able to focus directly on the quality of the results. This thesis is divided into chapters as a detailed description of how a similar problem can be approached in any other project that works with a certain form of knowledge base. Initial input analysis with access search for comparison of the various elements passes through a description of the default application state to a specific solution design. This should be a guideline for the implementation itself and for the testing of the proposed and implemented procedures.

Appliqué à des problèmes actuels de recherche pharmaceutique, ce mémoire traite de la génération de signatures de biomarqueurs par une approche d'extraction de règles d'association et une Analyse Formelle de Concepts. Elle a aboutit au développement d'une méthodologie qui a été validée par six projets de recherche de signatures de biomarqueurs.Alors qu'il n'existe pas de méthode optimale pour traiter les données biomarqueurs, cette méthodologie logique s'appuie sur un scénario global d'analyse déployant quatre méthodes, chacune dépendante de procédés différents. Cette architecture qualifie une problématique centrale de manière à optimiser la qualité d'une solution aux différents problèmes scientifiques posés. Les six applications pratiques ont démontré l'intérêt de la prise en compte précoce des critères de qualité énoncés par les experts du domaine. L'interactivité est soutenue tout au long du processus de découverte et produit des résultats imprévus pour l'expert. La méthodologie s'inscrit dans la lignée des approches dédiées à la stratification systématique des individus, qui constitue le premier palier vers une médecine personnalisée. / In the framework of current intricate questions to be solved by the pharmaceutical industry, this manuscript examines the generation of biomarker signatures through an approach that combines association rules extraction and Formal Concept Analysis. It led to the development of a methodology which was validated by six research industrial projects. While there is no single optimal method to handle biomarkers datasets, this logical methodology relies on a global datamining scenario made up of four different methods. Each method utilizes different processes. This architecture qualifies global approach that helps to optimize a response to different biomarker signatures discovery problems. The six applications presented in this manuscript demonstrate the interest of an early consideration of the quality criteria are expressed by the experts in the field. The interactivity is supported throughout the process of discovery and produces unexpected results for the expert. The methodology helps the systematic stratification of individuals, which constitutes the first step towards personalized medicine.

Orientador: Antônio Batocchio / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-27T18:08:13Z (GMT). No. of bitstreams: 1 Miguel_CarlosHenrique_M.pdf: 3528187 bytes, checksum: 165344ab93862eb94649f13d1f4a8626 (MD5) Previous issue date: 2015 / Resumo: O objetivo do trabalho foi elaborar um método de identificação de perfis de produto que representa os grupos de características frequentes do produto nas compras efetuadas por seus clientes. Foi feita uma revisão de literatura sobre quais áreas de gestão são influenciadas pela identificação de perfis de produtos, dentre elas: Planejamento de Demanda, Cadeia de Valor, Cadeia de Suprimentos e Cadeia Logística. Mais especificamente, as subáreas mais afetadas são Entrega de Fornecedores Chaves em base no Just In Time e Sistema de Reposição Contínua. As tecnologias de identificação eletrônica de produtos produzidos em série (e. g. RF ID, código de barras e código QR) são formas de identificar cada venda de produto a ser utilizado pelo método. Dentre as técnicas aplicadas no método, os Conjuntos Fuzzy foram utilizados para categorizar as características quantitativas dos produtos, que passaram a ser a entrada para a Análise de Carrinho de Compras, possibilitando determinar cada perfil de produto através de mineração de dados por regras de associação. O Apriori foi um algoritmo apropriado para realizar a Análise de Carrinho de Compras, pois realiza mineração por regras de associação de conjunto de itens frequentes utilizando as regras de interesse: suporte, confiança e lift. O algoritmo está presente no pacote Arules do programa estatístico R. O pacote ArulesViz, que está presente no programa estatístico R, permite visualizar de forma gráfica os relacionamentos entre os itens do produto. O método foi aplicado a uma base de dados de pesquisa do setor automobilístico, retornando com sucesso os perfis de automóvel frequentes dentre as compras efetuadas pelos clientes / Abstract: This study aimed to prepare a product profile identification method representing the groups of common characteristics of the product in the purchases made by its customers. A literature review was made on which areas of management are influenced by the identification of product profiles, such as: Demand Planning, Value Chain, Supply Chain and Logistic Chain. Specifically the Keys Suppliers Delivery sub-areas based on Just in Time and Continuous Replacement System are the most affected. The electronic identification technologies of products produced in series (e.g. RF ID, barcode and QR code) are ways to identify each product sale to be used by the method. Among the techniques applied in the method, Fuzzy Sets were used to categorize the quantitative characteristics of the products, which are now the entrance to the Market Basket Analysis, allowing to find each product profile through data mining for association rules. The Apriori was an appropriate algorithm to perform Market Basket Analysis, as done by mining association rule set of frequent item sets using the rules of interest: support, confidence and lift. The algorithm is present in Arules package of statistical software R. The ArulesViz package, which is present in the R statistical software, displays graphically the relationships between the items of the product. The method was applied to a research database of the automotive sector successfully returning the frequent car profiles from purchases made by customers / Mestrado / Materiais e Processos de Fabricação / Mestre em Engenharia Mecânica

This thesis focuses on the problematics of implementing a multilevel association rules mining module, for existing data mining project. There are two main algorithms explained, Apriori and MLT2L1. The thesis continues with the datamining module implementation, as well as the DMSL elements design. In the final chapters deal with an example dataminig task and its result comparison as well as the whole thesis achievement description.

This master's thesis deals with questions of the data mining and an extension of a data mining system in the Oracle environment developed at FIT. So far, this system cannot apply to real-life conditions as there are no data mining modules available. This system's core application design includes an interface allowing the addition of mining modules. Until now, this interface has been tested on a sample mining module only; this module has not been executing any activity just demonstrating the use of this interface. The main focus of this thesis is the study of this interface and the implementation of a functional mining module testing the applicability of the implemented interface. Association rule mining module was selected for implementation.

This Master's Thesis deals with the integration of Business Intelligence tools into an information system. There are concepts of BI, data warehouses, the OLAP analysis introduced as well as the knowledge discovery from databases, especially the association rule mining. In the chapters focused on practical part of the thesis, the design and implementation of resultant application are depicted. There are also the applied technologies like i.e. Microsoft SQL Server 2005 described.

The main motivation for the study described in this dissertation is to benefit from the development in technology and the huge amount of available data which can be easily captured, stored and maintained electronically. We concentrate on Web usage (i.e., log) mining and Web structure mining. Analysing Web log data will reveal valuable feedback reflecting how effective the current structure of a web site is and to help the owner of a web site in understanding the behaviour of the web site visitors. We developed a framework that integrates statistical analysis, frequent pattern mining, clustering, classification and network construction and analysis. We concentrated on the statistical data related to the visitors and how they surf and pass through the various pages of a given web site to land at some target pages. Further, the frequent pattern mining technique was used to study the relationship between the various pages constituting a given web site. Clustering is used to study the similarity of users and pages. Classification suggests a target class for a given new entity by comparing the characteristics of the new entity to those of the known classes. Network construction and analysis is also employed to identify and investigate the links between the various pages constituting a Web site by constructing a network based on the frequency of access to the Web pages such that pages get linked in the network if they are identified in the result of the frequent pattern mining process as frequently accessed together. The knowledge discovered by analysing a web site and its related data should be considered valuable for online shoppers and commercial web site owners. Benefitting from the outcome of the study, a recommendation system was developed to suggest pages to visitors based on their profiles as compared to similar profiles of other visitors. The conducted experiments using popular datasets demonstrate the applicability and effectiveness of the proposed framework for Web mining and recommendation. As a by product of the proposed method, we demonstrate how it is effective in another domain for feature reduction by concentrating on gene expression data analysis as an application with some interesting results reported in Chapter 5.

由於台灣國人所得提高，生活水準跟著日漸提高，近年來更是意識到健康對個人及家庭的重要性，因此國內健康食品與藥品市場在這幾年蓬勃地發展，特別是連鎖藥妝的普及，結合藥品、健康食品與開架式保養品、化妝品銷售，提供專業藥師諮詢服務，成為複合式的經營模式。但近年來連鎖藥妝零售業者也面臨來自外商連鎖藥妝、本土連鎖藥妝、地區性連鎖藥局等不同體系的競爭，因此藥品及化粧品零售業者普遍認同，目前經營上所面臨之困難主要為「同業競爭激烈」。 商品力為一連鎖藥妝零售業者成功的重要因素，具體展現在商品多樣性、商品獲利性、商品價格競爭力、商品獨特性…等不同的面向。目前藥品及化粧品零售業中，確實大部分的業者都有商品企劃或設計的需求，但有商品企劃或設計部門者僅為少數。利用資料採礦技術，將能在不大量增加人事費用的情況下，有效率地協助進行商品企劃或設計，進而提升連鎖藥妝零售業者的商品力。 本研究將針對資料採礦在連鎖藥妝上的應用進行探討，包含以下研究目的： 1. 利用資料採礦中之集群分析建置商品力矩陣，代表他們的屬性與價值。透過商品力矩陣釐清各商品的定位，幫助決策者優化商品組合，針對各商品執行妥善策略安排。 2. 依循集群分析後的結果，更進一步進行商品分類的關聯規則分析。幫助決策者將集群分析之成果化為實務決策之參考，優化商品組合，針對各商品執行妥善策略安排，也為關聯規則的整理帶來新的應用方式。 3. 根據上述兩模型建置之結果，對H連鎖藥妝提出具體可行之行銷策略建議。 本研究利用資料採礦中的Two-step Cluster模型建置出H連鎖藥妝中各項商品的商品力矩陣，此矩陣的兩軸分別為「個別商品的平均毛利」及「個別商品的年交易筆數」，將各種商品概略分為明星、樂透、忠狗、問號四大類商品，分別代表他們不同的屬性與價值。同時配合關聯規則分析，提出具體可行之候選規則篩選模式： 1. 樂透型商品，應用方式有兩種，將樂透型商品放在Apriori模型中的後項，找出導購向樂透型商品的潛在模式；將樂透型商品放在Apriori模型中的前項，並將後項商品作為加價購搭售促銷標的，提升購買樂透型商品的意願。 2. 忠狗型商品，應用方式也有兩種，將忠狗型商品放在Apriori模型中的前項，找出可能導購的商品標的，推出合適的加價購搭售促銷活動；另外也可以藉由觀察忠狗型商品的消費行為，進而提供適當的促銷、推薦，提高其他品項交叉銷售的可能性。 / Taiwanese living standard raised due to the income growing, which lead to recognizing the importance of health toward personal and family. As a result, the market of dietary supplements and drugs flourishing these years, especially the spread of chained drugstores, which turned into combinative store by providing professional pharmacist consultant and selling of drugs, dietary supplements, skincare products and cosmetics. The drug and cosmetic retailers generally agreed that the main difficulty is “Industry Competition” due to the competition from different systems, including foreign chained drugstores, local chained drugstores and regional chained drugstores. Commodity competitiveness is one of the key successful factors of chained drugstores, which expressed as commodity diversity, commodity profitability, commodity price competitiveness, commodity uniqueness, etc. Seldom drugstores own product planning or designing department although most drugstores have demand of product planning or designing. It could raise the commodity competitiveness of chained drugstores by applying data mining to help product planning or designing more efficiently without increasing too much labor cost. This study focus on the application of data mining on chained drugstores, including goals below: 1. Building commodity competitiveness matrix by cluster analysis, representing their features and values. Through positioning products on commodity competitiveness matrix, helping decision maker optimize product mix and execute appropriate strategy toward products. 2. Based on the results from cluster analysis, proceed association rules analysis toward product categories. Help turning the results from cluster analysis into references of actual decision, optimize product mix and execute appropriate strategy toward products. Bringing new application pattern of association rules analysis. 3. Providing actual marketing strategy suggestions to H chained drugstore based on the two models built above. This study built commodity competitiveness matrix of H chained drugstore by Two-step Cluster model, which take “average margin of individual product” and “annual transaction amounts of individual product” as two axes. Divided products into Star, Lottery, Greyfriars and Question Mark. Each of them represent different features and values. Providing practical filtering rules of candidate rules in association rules analysis: 1. Lottery Products: Placing lottery products as consequents in Apriori model, searching for the potential pattern led to buying lottery products. Placing lottery products as antecedents, which we can provide the consequents with additional purchase discount in order to raise the willing to buy lottery products. 2. Greyfriars Products: Placing Greyfriars products as antecedents, searching for potential recommendation with additional purchase discount. Providing appropriate sales and recommendation to raise the possibility of cross-selling by observing consuming behaviors of Greyfriars products.

Posterior analysis in Bayesian model averaging (BMA) applications often includes the assessment of measures of jointness (joint inclusion) across covariates. We link the discussion of jointness measures in the econometric literature to the literature on association rules in data mining exercises. We analyze a group of alternative jointness measures that include those proposed in the BMA literature and several others put forward in the field of data mining. The way these measures address the joint exclusion of covariates appears particularly important in terms of the conclusions that can be drawn from them. Using a dataset of economic growth determinants, we assess how the measurement of jointness in BMA can affect inference about the structure of bivariate inclusion patterns across covariates. (authors' abstract) / Series: Department of Economics Working Paper Series