Dissertations / Theses on the topic 'Associative classification rule base'

Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).

There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters. The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches. Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization.

Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.
Applied Science University (ASU) of Jordan

Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.

Classification using association rules is a research field in data mining that primarily uses association rule discovery techniques in classification benchmarks. It has been confirmed by many research studies in the literature that classification using association tends to generate more predictive classification systems than traditional classification data mining techniques like probabilistic, statistical and decision tree. In this thesis, we introduce a novel data mining algorithm based on classification using association called “Looking at the Class” (LC), which can be used in for mining a range of classification data sets. Unlike known algorithms in classification using the association approach such as Classification based on Association rule (CBA) system and Classification based on Predictive Association (CPAR) system, which merge disjoint items in the rule learning step without anticipating the class label similarity, the proposed algorithm merges only items with identical class labels. This saves too many unnecessary items combining during the rule learning step, and consequently results in large saving in computational time and memory. Furthermore, the LC algorithm uses a novel prediction procedure that employs multiple rules to make the prediction decision instead of a single rule. The proposed algorithm has been evaluated thoroughly on real world security data sets collected using an automated tool developed at Huddersfield University. The security application which we have considered in this thesis is about categorizing websites based on their features to legitimate or fake which is a typical binary classification problem. Also, experimental results on a number of UCI data sets have been conducted and the measures used for evaluation is the classification accuracy, memory usage, and others. The results show that LC algorithm outperformed traditional classification algorithms such as C4.5, PART and Naïve Bayes as well as known classification based association algorithms like CBA with respect to classification accuracy, memory usage, and execution time on most data sets we consider.

Recent studies in data mining revealed that Associative Classification (AC) data mining approach builds competitive classification classifiers with reference to accuracy when compared to classic classification approaches including decision tree and rule based. Nevertheless, AC algorithms suffer from a number of known defects as the generation of large number of rules which makes it hard for end-user to maintain and understand its outcome and the possible over-fitting issue caused by the confidence-based rule evaluation used by AC. This thesis attempts to deal with above problems by presenting five new pruning methods, prediction method and employs them in an AC algorithm that significantly reduces the number of generated rules without having large impact on the prediction rate of the classifiers. Particularly, the new pruning methods that discard redundant and insignificant rules during building the classifier are employed. These pruning procedures remove any rule that either has no training case coverage or covers a training case without the requirement of class similarity between the rule class and that of the training case. This enables large coverage for each rule and reduces overfitting as well as construct accurate and moderated size classifiers. Beside, a novel class assignment method based on multiple rules is proposed which employs group of rule to make the prediction decision. The integration of both the pruning and prediction procedures has been used to enhanced a known AC algorithm called Multiple-class Classification based on Association Rules (MCAR) and resulted in competent model in regard to accuracy and classifier size called " Multiple-class Classification based on Association Rules 2(MCAR2)". Experimental results against different datasets from the UCI data repository showed that the predictive power of the resulting classifiers in MCAR2 slightly increase and the resulting classifier size gets reduced comparing with other AC algorithms such as Multiple-class Classification based on Association Rules (MCAR).

Associative Classification (AC) in data mining is a rule based approach that uses association rule techniques to construct accurate classification systems (classifiers). The majority of existing AC algorithms extract one class per rule and ignore other class labels even when they have large data representation. Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the exponential growth of rules in AC has been investigated in this thesis aiming to minimise the number of candidate rules, and therefore reducing the classifier size so end-user can easily exploit and maintain it. Moreover, an investigation to both rule ranking and test data classification steps have been conducted in order to improve the performance of AC algorithms in regards to predictive accuracy. Overall, this thesis investigates different problems related to AC not limited to the ones listed above, and the results are new AC algorithms that devise single and multi-label rules from different applications data sets, together with comprehensive experimental results. To be exact, the first algorithm proposed named Multi-class Associative Classifier (MAC): This algorithm derives classifiers where each rule is connected with a single class from a training data set. MAC enhanced the rule discovery, rule ranking, rule filtering and classification of test data in AC. The second algorithm proposed is called Multi-label Classifier based Associative Classification (MCAC) that adds on MAC a novel rule discovery method which discovers multi-label rules from single label data without learning from parts of the training data set. These rules denote vital information ignored by most current AC algorithms which benefit both the end-user and the classifier's predictive accuracy. Lastly, the vital problem related to web threats called 'website phishing detection' was deeply investigated where a technical solution based on AC has been introduced in Chapter 6. Particularly, we were able to detect new type of knowledge and enhance the detection rate with respect to error rate using our proposed algorithms and against a large collected phishing data set. Thorough experimental tests utilising large numbers of University of California Irvine (UCI) data sets and a variety of real application data collections related to website classification and trainer timetabling problems reveal that MAC and MCAC generates better quality classifiers if compared with other AC and rule based algorithms with respect to various evaluation measures, i.e. error rate, Label-Weight, Any-Label, number of rules, etc. This is mainly due to the different improvements related to rule discovery, rule filtering, rule sorting, classification step, and more importantly the new type of knowledge associated with the proposed algorithms. Most chapters in this thesis have been disseminated or under review in journals and refereed conference proceedings.

In the currently highly competitive environment, the information systems of the businesses should not only effectively support the existing business processes, but also have to be dynamically adaptable to the changes in the environment. There are increasing efforts at separation of the application and the business logic in the information system. One of the appropriate instruments for this separation is the business rule approach. Business rules are simple, understandable rules. They can be used for the knowledge externalization and sharing also as for the active control and decisions within the business processes. Although the business rule approach is used for almost 20 years, the various specifications and practical applications of business rules are still a goal of the active research. The disadvantage of the business rule approach is great demands on obtaining of the rules. There has to be a domain expert, who is able to manually write them. One of the problems addressed by the current research is the possibility of (semi)automatic acquisition of business rules from the different resources - unstructured documents, historical data etc. This dissertation thesis addresses the problem of acquisition (learning) of business rules from the historical data of the company. The main objective of this thesis is to design and validate a method for (semi)automatic learning of business rules using the data mining of association rules. Association rule are a known data mining method for discovering of interesting relations hidden in the data. Association rules are comprehensible and explainable. The comprehensibility of association rules is suitable for the use of them for learning of business rules. For this purpose the user can use not only simple rules discovered using the algorithm Apriori or FP-Growth, but also more complex association rules discovered using the GUHA method. Within this thesis is used the procedure 4ft-Miner implemented in the data mining system LISp Miner. The first part of this thesis contains the description of the relevant topics from the research of business rules and association rules. Business rules is not a name of one specification of standard but rather a label of the approach to modelling of business logic. As part of the work there is defined a process of selection of the most appropriate specification of business rules for the selected practical use. Consequently, the author proposed three models of involving of data mining of association rules into business rule sets. These models contain also the definition of a model for the transformation of GUHA association rules in the business rules for the system JBoss Drools. For the possibility of learning of business rules using the data mining results from more than one data set, the author proposed a knowledge base. The knowledge base is suitable for the interconnection of business rules and data mining of association rules. From the perspective of business rules the knowledge base is a term dictionary. From the perspective of data mining the knowledge base contains some background knowledge for data preprocessing and preparation of classification models. The proposed models have been validated using practical implementations in the systems EasyMiner (in conjunction with JBoss Drools) and Erian. The thesis contains also a description of two model use cases based on real data from the field of marketing and the field of health insurance.

Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies.

Dans de nombreux problèmes de classification, les données sont intrinsèquement incertaines. Les données d’apprentissage disponibles peuvent être imprécises, incomplètes, ou même peu fiables. En outre, des connaissances spécialisées partielles qui caractérisent le problème de classification peuvent également être disponibles. Ces différents types d’incertitude posent de grands défis pour la conception de classifieurs. La théorie des fonctions de croyance fournit un cadre rigoureux et élégant pour la représentation et la combinaison d’une grande variété d’informations incertaines. Dans cette thèse, nous utilisons cette théorie pour résoudre les problèmes de classification des données incertaines sur la base de deux approches courantes, à savoir, la méthode des k plus proches voisins (kNN) et la méthode à base de règles.Pour la méthode kNN, une préoccupation est que les données d’apprentissage imprécises dans les régions où les classes de chevauchent peuvent affecter ses performances de manière importante. Une méthode d’édition a été développée dans le cadre de la théorie des fonctions de croyance pour modéliser l’information imprécise apportée par les échantillons dans les régions qui se chevauchent. Une autre considération est que, parfois, seul un ensemble de données d’apprentissage incomplet est disponible, auquel cas les performances de la méthode kNN se dégradent considérablement. Motivé par ce problème, nous avons développé une méthode de fusion efficace pour combiner un ensemble de classifieurs kNN couplés utilisant des métriques couplées apprises localement. Pour la méthode à base de règles, afin d’améliorer sa performance dans les applications complexes, nous étendons la méthode traditionnelle dans le cadre des fonctions de croyance. Nous développons un système de classification fondé sur des règles de croyance pour traiter des informations incertains dans les problèmes de classification complexes. En outre, dans certaines applications, en plus de données d’apprentissage, des connaissances expertes peuvent également être disponibles. Nous avons donc développé un système de classification hybride fondé sur des règles de croyance permettant d’utiliser ces deux types d’information pour la classification
In many classification problems, data are inherently uncertain. The available training data might be imprecise, incomplete, even unreliable. Besides, partial expert knowledge characterizing the classification problem may also be available. These different types of uncertainty bring great challenges to classifier design. The theory of belief functions provides a well-founded and elegant framework to represent and combine a large variety of uncertain information. In this thesis, we use this theory to address the uncertain data classification problems based on two popular approaches, i.e., the k-nearest neighbor rule (kNN) andrule-based classification systems. For the kNN rule, one concern is that the imprecise training data in class over lapping regions may greatly affect its performance. An evidential editing version of the kNNrule was developed based on the theory of belief functions in order to well model the imprecise information for those samples in over lapping regions. Another consideration is that, sometimes, only an incomplete training data set is available, in which case the ideal behaviors of the kNN rule degrade dramatically. Motivated by this problem, we designedan evidential fusion scheme for combining a group of pairwise kNN classifiers developed based on locally learned pairwise distance metrics.For rule-based classification systems, in order to improving their performance in complex applications, we extended the traditional fuzzy rule-based classification system in the framework of belief functions and develop a belief rule-based classification system to address uncertain information in complex classification problems. Further, considering that in some applications, apart from training data collected by sensors, partial expert knowledge can also be available, a hybrid belief rule-based classification system was developed to make use of these two types of information jointly for classification

A extração de regras de associação (ARM - Association Rule Mining) de dados quantitativos tem sido pesquisa de grande interesse na área de mineração de dados. Com o crescente aumento das bases de dados, há um grande investimento na área de pesquisa na criação de algoritmos para melhorar o desempenho relacionado a quantidade de regras, sua relevância e a performance computacional. O algoritmo APRIORI, tradicionalmente usado na extração de regras de associação, foi criado originalmente para trabalhar com atributos categóricos. Geralmente, para usá-lo com atributos contínuos, ou quantitativos, é necessário transformar os atributos contínuos, discretizando-os e, portanto, criando categorias a partir dos intervalos discretos. Os métodos mais tradicionais de discretização produzem intervalos com fronteiras sharp, que podem subestimar ou superestimar elementos próximos dos limites das partições, e portanto levar a uma representação imprecisa de semântica. Uma maneira de tratar este problema é criar partições soft, com limites suavizados. Neste trabalho é utilizada uma partição fuzzy das variáveis contínuas, que baseia-se na teoria dos conjuntos fuzzy e transforma os atributos quantitativos em partições de termos linguísticos. Os algoritmos de mineração de regras de associação fuzzy (FARM - Fuzzy Association Rule Mining) trabalham com este princípio e, neste trabalho, o algoritmo FUZZYAPRIORI, que pertence a esta categoria, é utilizado. As regras extraídas são expressas em termos linguísticos, o que é mais natural e interpretável pelo raciocício humano. Os algoritmos APRIORI tradicional e FUZZYAPRIORI são comparado, através de classificadores associativos, baseados em regras extraídas por estes algoritmos. Estes classificadores foram aplicados em uma base de dados relativa a registros de conexões TCP/IP que destina-se à criação de um Sistema de Detecção de Intrusos.
The mining of association rules of quantitative data has been of great research interest in the area of data mining. With the increasing size of databases, there is a large investment in research in creating algorithms to improve performance related to the amount of rules, its relevance and computational performance. The APRIORI algorithm, traditionally used in the extraction of association rules, was originally created to work with categorical attributes. In order to use continuous attributes, it is necessary to transform the continuous attributes, through discretization, into categorical attributes, where each categorie corresponds to a discrete interval. The more traditional discretization methods produce intervals with sharp boundaries, which may underestimate or overestimate elements near the boundaries of the partitions, therefore inducing an inaccurate semantical representation. One way to address this problem is to create soft partitions with smoothed boundaries. In this work, a fuzzy partition of continuous variables, which is based on fuzzy set theory is used. The algorithms for mining fuzzy association rules (FARM - Fuzzy Association Rule Mining) work with this principle, and, in this work, the FUZZYAPRIORI algorithm is used. In this dissertation, we compare the traditional APRIORI and the FUZZYAPRIORI, through classification results of associative classifiers based on rules extracted by these algorithms. These classifiers were applied to a database of records relating to TCP / IP connections that aims to create an Intrusion Detection System.

Cette thèse explique et présente notre démarche de la réalisation d’un système à base de règles de reconnaissance et de classification automatique des EN en arabe. C’est un travail qui implique deux disciplines : la linguistique et l’informatique. L’outil informatique et les règles la linguistiques s’accouplent pour donner naissance à une nouvelle discipline ; celle de « traitement automatique des langues », qui opère sur des niveaux différents (morphosyntaxique, syntaxique, sémantique, syntactico-sémantique etc.). Nous avons donc, dans ce qui nous concerne, mis en œuvre des informations et règles linguistiques nécessaires au service du logiciel informatique, qui doit être en mesure de les appliquer, pour extraire et classifier, par des annotations syntaxiques et/ou sémantiques, les différentes classes d’entités nommées.Ce travail de thèse s’inscrit donc dans un cadre général de traitement automatique des langues, mais plus particulièrement dans la continuité des travaux réalisés au niveau de l’analyse morphosyntaxique par la conception et la réalisation des bases des données lexicales SAMIA et ensuite DIINAR avec l’ensemble de résultats de recherches qui en découlent. C’est une tâche qui vise à l’enrichissement lexical par des entités nommées simples et complexes, et qui veut établir la transition de l’analyse morphosyntaxique vers l’analyse syntaxique, et syntatico-sémantique dans une visée plus générale de l’analyse du contenu textuel. Pour comprendre de quoi il s’agit, il nous était important de commencer par la définition de l’entité nommée. Et pour mener à bien notre démarche, nous avons distingué entre deux types principaux : pur nom propre et EN descriptive. Nous avons aussi établi une classification référentielle en se basant sur diverses classes et sous-classes qui constituent la référence de nos annotations sémantiques. Cependant, nous avons dû faire face à deux difficultés majeures : l’ambiguïté lexicale et les frontières des entités nommées complexes. Notre système adopte une approche à base de règles syntactico-sémantiques. Il est constitué, après le Niveau 0 d’analyse morphosyntaxique, de cinq niveaux de construction de patrons syntaxiques et syntactico-sémantiques basés sur les informations linguistique nécessaires (morphosyntaxiques, syntaxiques, sémantique, et syntactico-sémantique). Ce travail, après évaluation en utilisant deux corpus, a abouti à de très bons résultats en précision, en rappel et en F–mesure. Les résultats de notre système ont un apport intéressant dans différents application du traitement automatique des langues notamment les deux tâches de recherche et d’extraction d’informations. En effet, on les a concrètement exploités dans les deux applications (recherche et extraction d’informations). En plus de cette expérience unique, nous envisageons par la suite étendre notre système à l’extraction et la classification des phrases dans lesquelles, les entités classifiées, principalement les entités nommées et les verbes, jouent respectivement le rôle d’arguments et de prédicats. Un deuxième objectif consiste à l’enrichissement des différents types de ressources lexicales à l’instar des ontologies
This thesis explains and presents our approach of rule-based system of arabic named entity recognition and classification. This work involves two disciplines : linguistics and computer science. Computer tools and linguistic rules are merged to give birth to a new discipline : Natural Languge Processsing, which operates in different levels (morphosyntactic, syntactic, semantic, syntactico-semantic…). So, in our particular case, we have put the necessary linguistic information and rules to software sevice. This later should be able to apply and implement them in order to recognise and classify, by syntactic and semantic annotations, the different named entity classes.This work of thesis is incorporated within the general domain of natural language processing, but it particularly falls within the scope of the continuity of the accomplished work in terms of morphosyntactic analysis and the realisation of lexical data bases of SAMIA and then DIINAR as well as the accompanying scientific recearch. This task aimes at lexical enrichement with simple and complex named entities and at establishing the transition from the morphological analysis into syntactic and syntactico-semantic analysis. The ultimate objective is text analysis. To understand what it is about, it was important to start with named entity definition. To carry out this task, we distinguished between two main named entity types : pur proper name and descriptive named entities. We have also established a referential classification on the basis of different classes and sub-classes which constitue the reference for our semantic annotations. Nevertheless, we are confronted with two major difficulties : lexical ambiguity and the frontiers of complex named entities. Our system adoptes a syntactico-semantic rule-based approach. After Level 0 of morpho-syntactic analysis, the system is made up of five levels of syntactic and syntactico-semantic patterns based on tne necessary linguisic information (i.e. morphosyntactic, syntactic, semantic and syntactico-semantic information).This work has obtained very good results in termes of precision, recall and F-measure. The output of our system has an interesting contribution in different applications of the natural language processing especially in both tasks of information retrieval and information extraction. In fact, we have concretely exploited our system output in both applications (information retrieval and information extraction). In addition to this unique experience, we envisage in the future work to extend our system into the sentence extraction and classification, in which classified entities, mainly named entities and verbs, play respectively the role of arguments and predicates. The second objective consists in the enrichment of different types of lexical resources such as ontologies

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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Os resultados dos problemas de classificação por regras de associação sofrem grande influência da estrutura dos dados que estão sendo utilizados. Uma dificuldade na área é a resolução de problemas de classificação quando se trata de bases de dados desbalanceadas. Assim, o presente trabalho apresenta um estudo sobre desbalanceamento em bases de dados transacionais, abordando os principais métodos utilizados na resolução do problema de desbalanceamento. Além disso, no que tange ao desbalanceamento, este trabalho propõe um modelo para realizar o balanceamento entre classes, sendo realizados experimentos com diferentes mé- todos de balanceamento e métodos ensemble, baseados em comitê de classificadores. Tais experimentos foram realizados em bases transacionais e não transacionais com o intuito de validar o modelo proposto e melhorar a predição do algoritmo de classificação por regras de associação. Bases de dados não transacionais também foram utilizadas nos ex perimentos, com o objetivo de verificar o comportamento do modelo proposto em tais bases. Outro fator importante no processo de classificação é a dimensão da base de dados que, quando muito grande, pode comprometer o desempenho dos classificadores. Neste traba lho, também é proposto um modelo de seleção de características baseado na classificação por regras de associação. Para validar o modelo proposto, também foram realizados ex- perimentos aplicando diferentes métodos de seleção nas bases de dados. Os resultados da classificação obtidos utilizando as bases contendo as características selecionadas pelos me- todos, foram comparados para validar o modelo proposto, tais resultados apresentaram-se satisfatórios em relação aos demais métodos de seleção.
The results of Classification Based on Associations Rules (CBA) are greatly influenced by the used data structure. A difficulty in this area is solving classification problems when it comes to unbalanced databases. Thus, this paper presents a study of unbalance in transactional and non-transactional databases, addressing the main methods used to solve the unbalance problem. In addition, with respect to the unbalance problem, this paper proposes a model to reach the balance between classes, conducting experiments with different methods of balancing and ensemble methods based on classifiers committee. These experiments were performed in transactional and non-transactional databases, in order to validate the proposed model and improve Classification Based on Associations Rules prediction. Another important factor in the classification process is database dimensionality, be cause when too large, it can compromise the classifiers performance. In this work, it is also proposed a feature selection model based on the rules of CBA. Aiming to validate this model, experiments were also performed applying different features selection methods in the databases.The classification results obtained using the bases containing the features selected by the methods were compared to validate the proposed model, these results were satisfactory in comparison with other methods of selection.

L'extraction de règles d'association et de bi-clusters sont deux techniques de fouille de données complémentaires majeures, notamment pour l'intégration de connaissances. Ces techniques sont utilisées dans de nombreux domaines, mais aucune approche permettant de les unifier n'a été proposée. Hors, réaliser ces extractions indépendamment pose les problèmes des ressources nécessaires (mémoire, temps d'exécution et accès aux données) et de l'unification des résultats. Nous proposons une approche originale pour extraire différentes catégories de modèles de connaissances tout en utilisant un minimum de ressources. Cette approche est basée sur la théorie des ensembles fermés et utilise une nouvelle structure de données pour extraire des représentations conceptuelles minimales de règles d'association, bi-clusters et règles de classification. Ces modèles étendent les règles d'association et de classification et les bi-clusters classiques, les listes d'objets supportant chaque modèle et les relations hiérarchiques entre modèles étant également extraits. Cette approche a été appliquée pour l'analyse de données d'interaction protéomiques entre le virus VIH-1 et l'homme. L'analyse de ces interactions entre espèces est un défi majeur récent en bio-informatique. Plusieurs bases de données intégrant des informations hétérogènes sur les interactions et des connaissances biologiques sur les protéines ont été construites. Les résultats expérimentaux montrent que l'approche proposée peut traiter efficacement ces bases de données et que les modèles conceptuels extraits peuvent aider à la compréhension et à l'analyse de la nature des relations entre les protéines interagissant
Knowledge pattern extraction is one of the major topics in the data mining and background knowledge integration domains. Out of several data mining techniques, association rule mining and bi-clustering are two major complementary tasks for these topics. These tasks gained much importance in many domains in recent years. However, no approach was proposed to perform them in one process. This poses the problems of resources required (memory, execution times and data accesses) to perform independent extractions and of the unification of the different results. We propose an original approach for extracting different categories of knowledge patterns while using minimum resources. This approach is based on the frequent closed patterns theoretical framework and uses a novel suffix-tree based data structure to extract conceptual minimal representations of association rules, bi-clusters and classification rules. These patterns extend the classical frameworks of association and classification rules, and bi-clusters as data objects supporting each pattern and hierarchical relationships between patterns are also extracted. This approach was applied to the analysis of HIV-1 and human protein-protein interaction data. Analyzing such inter-species protein interactions is a recent major challenge in computational biology. Databases integrating heterogeneous interaction information and biological background knowledge on proteins have been constructed. Experimental results show that the proposed approach can efficiently process these databases and that extracted conceptual patterns can help the understanding and analysis of the nature of relationships between interacting proteins

La découverte de motifs est une tâche importante en fouille de données. Cemémoire traite de l’extraction des motifs émergents minimaux. Nous proposons une nouvelleméthode efficace qui permet d’extraire les motifs émergents minimaux sans ou avec contraintede support ; contrairement aux méthodes existantes qui extraient généralement les motifs émergentsminimaux les plus supportés, au risque de passer à côté de motifs très intéressants maispeu supportés par les données. De plus, notre méthode prend en compte l’absence d’attributqui apporte une nouvelle connaissance intéressante.En considérant les règles associées aux motifs émergents avec un support élevé comme desrègles prototypes, on a montré expérimentalement que cet ensemble de règles possède unebonne confiance sur les objets couverts mais malheureusement ne couvre pas une bonne partiedes objets ; ce qui constitue un frein pour leur usage en classification. Nous proposons uneméthode de sélection à base de prototypes qui améliore la couverture de l’ensemble des règlesprototypes sans pour autant dégrader leur confiance. Au vu des résultats encourageants obtenus,nous appliquons cette méthode de sélection sur un jeu de données chimique ayant rapport àl’environnement aquatique : Aquatox. Cela permet ainsi aux chimistes, dans un contexte declassification, de mieux expliquer la classification des molécules, qui sans cette méthode desélection serait prédites par l’usage d’une règle par défaut
Pattern discovery is an important field of Knowledge Discovery in Databases.This work deals with the extraction of minimal emerging patterns. We propose a new efficientmethod which allows to extract the minimal emerging patterns with or without constraint ofsupport ; unlike existing methods that typically extract the most supported minimal emergentpatterns, at the risk of missing interesting but less supported patterns. Moreover, our methodtakes into account the absence of attribute that brings a new interesting knowledge.Considering the rules associated with emerging patterns highly supported as prototype rules,we have experimentally shown that this set of rules has good confidence on the covered objectsbut unfortunately does not cover a significant part of the objects ; which is a disavadntagefor their use in classification. We propose a prototype-based selection method that improvesthe coverage of the set of the prototype rules without a significative loss on their confidence.We apply our prototype-based selection method to a chemical data relating to the aquaticenvironment : Aquatox. In a classification context, it allows chemists to better explain theclassification of molecules, which, without this method of selection, would be predicted by theuse of a default rule

碩士
國立臺灣大學
工業工程學研究所
93
Neural networks have been successfully applied to solve a variety of application problems including classification and function approximation. They are especially useful for function approximation problems because they have been shown to be universal approximators. But, The neural network is considered a black box. It is hard to determine if the learning result of a neural network is reasonable, and the network can not effectively help users to develop the domain knowledge. Thus, it is important to supply a reasonable and effective analytic method of the neural network. This research expects to be able to improve the black box shortcoming of the solving type neural network. Competitive Learning Neural Network include Self-Organized Map, Learning Vector Quantization. These common characteristics of network are that are all to adopt the single layer of neural networks that Winner-Take-All completely that their study rules .However, past researchs are mostly all limited on the neural network structure of the feedforward network, but the important degree that can''t know this rule. So this research develop to extract out the Association Classification Rule from neurons. Finally, extracted rule is compared decision tree-C4.5, proves with some BenchMark Problems in UCI Machine Learning DataBase that distinguish the correct rate.

碩士
淡江大學
資訊工程學系碩士在職專班
99
Although different associative classification algorithms have been proposed, none of the available associative classification algorithms consider the rule dependence problem that directly influences the classification accuracy of associative classification algorithms. Since the finding of the optimal execution order of class association rules (CARs) is a combinational problem, instead of finding the optimal execution order of CARs, in this paper we propose polynomial time algorithms to re-rank the execution order of CARs by rules’ priority. This reduces the influence of rule dependency problems. Consequently, the performance (the classification accuracy and recall rate) of the associative classification algorithms can be improved. The experimental results show that using LAZY with our method can get better classification results than that of the LAZY association classifier without considering the rule dependence problem.

碩士
淡江大學
資訊工程學系碩士在職專班
98
Applying Associative Rule on Text Classification, the rule ranking is generally in accordance with confidence, support and length of rules. However, most recent researches often ignore the issue of multiple classes, this study will adopt the general ranking with the condition of class and will have a discussion on the effect of text classification with our ranking method. Our data source is Reuters 21578 collection and the implementation steps as follow: 1.we will adopt Association Rule to discover all frequent ruleitems; 2. to prune and rank the rules by Lazy method; 3.to figure out all rule frequencies of each class for deciding the sequence of classes; 4.to build the associative classifier according to the class priority; 5.classifiy unseen test documents to verify the performance and have an observation of various class priority whether our method could improve the accuracy of associative classification or not.

碩士
國立臺灣師範大學
資訊教育研究所
89
Most previous works on image classification which are purposed for specific image domain, extract global image properties to be feature of an image. However, the global image properties can't represent objects and spatial features well. In this thesis, a kind of object-based image feature is designed, called Block Attribute Association Rules (BAAR), which indicates the relationship among locations and sizes of color blocks. First, the color domain of an image is transformed to HSV color space and quantized to be 148 colors. After that, color blocks and their content attributes are extracted efficiently by applying Block List. The Binary Relationship Counting Table (BRCT) is designed for computing the supports and confidences of BAARs efficently. Moreover, Dynamic Multi-Decision Tree (DMDT) algorithm is proposed for deriving classification rules, and a pruning algorithm is provided to reduce the number of classification rules. The proposed strateies are also extended to perform fuzzy classification. According to the experiment results, it shows that the classification accuracy of proposed classification methods is superior than C4.5 and fuzzy decision tree, and the proposed strateies are applicable on various image domains well.

碩士
淡江大學
資訊工程學系碩士班
98
In general, the approach in rule ranking of associative classification (AC)[1][2] begins first with confidence value in order of the highest to the lowest, then support value in order of the highest to the lowest, and finally rule in order of the shortest to the longest. In order to make more documents classifiable, short rules are ranked higher than long rules as short rules also have higher compatibility. With the use of discourse-based experiments in this study, it was found that common characteristics existed between certain categories and they were not always mutually associated. One could achieve a considerable degree of improvement by placing rules of a certain category in front of rules of another category. The core of this paper is centered on the issue of rule ranking. Apart from adopting the ranking method proposed by Lazy[3] method as the general principle, Multi-Level class priority was proposed to explore its impact on the classification performance. It was proven in the experiments that adding Multi-Level class priority in rule ranking would help to achieve better classification performance than any general ranking principles.

碩士
國立高雄應用科技大學
資訊管理系
98
The association classification was widely used for data mining, and had good performance. Usually, we need to set a threshold of support value to reduce the number of associative classification rule that required to be computed, and filtering some possible noise rules. However, it may lose some important associative rules with small value of support. If we only decrease the threshold of support value, we will obtain a large number of associative rule and some of them are harmful rules. Therefore, the threshold support value will effect for precision and execution performance significantly. In rule selecting, Wenmin Li et. al., purposed a classification based on multiple class-association rules (CMAR) approach. In CMAR approach, the computing of rule weight is the most important issues. If the computing of rule weight is biased in some situation, it would decrease the precision of classification. This paper combines the CMAR and LAC (Lazy Associative Classifier) algorithm to mining the small disjunction rules. Besides, this paper proposes a new rule group weighted computing approach to improve weighted bias problem in CMAR. In experimental part, we use UCI’s 26 dataset. The experiment results demonstrate the proposed algorithm works fairly well and the classification performance has significantly improved.

Trabalho de projeto
O presente trabalho de projeto pretende apresentar um Modelo integrado de Gestão das Finanças Públicas para Portugal que se constitua como uma alteração estrutural duradoura no âmbito das finanças públicas nacionais, permitindo contribuir para resolução dos seus problemas recorrentes, garantindo a sua transparência e a accountability de todos os envolvidos na sua gestão. Neste sentido, o presente trabalho estrutura-se em três capítulos. O primeiro capítulo apresenta uma perspetiva abrangente sobre o Estado e a Administração Pública ao longo tempo, desde o início da modernidade às mais recentes reformas. O segundo capítulo efetua o diagnóstico da situação atual da Gestão das Finanças Públicas (GFP) em Portugal através das redes que se podem observar e das fragilidades existentes. Por último, o terceiro capítulo apresenta uma proposta de melhoria que pela construção de um modelo integrado de GFP em Portugal, efetuando um enquadramento genérico do mesmo e desenvolvendo os aspetos de integração, atendendo ao aconselhado pelas boas práticas internacionais, e estabelecendo as diretrizes principais do funcionamento das redes identificadas no âmbito do modelo. O modelo apresentado pretende resolver as fragilidades identificadas através da integração das funções da GFP: Contabilidade, Orçamento e Tesouro. Para tal é necessária a utilização pela plenitude das administrações públicas da contabilidade com base de acréscimo assente num plano de contas adequado, a revisão dos classificadores do Orçamento do Estado (OE) e a adoção de uma boa governança do OE, o desenvolvimento de um sistema de conta única do Tesouro, no contexto de uma gestão de tesouraria moderna.
This research project intends to present an integrated model of Public Financial Management for Portugal as a lasting structural change in national public finances, concurring to solve their recurring problems, ensuring the transparency and accountability of all involved in its management. In this sense, this paper is divided into three chapters. The first chapter presents a comprehensive perspective on the state and public administration over time, since the beginning of modernity to the latest reforms. The second chapter makes the diagnosis of the current situation of the Public Financial Management (PFM) in Portugal through the networks that can be observed and existing weaknesses. Finally, the third chapter presents a proposal to improve it by building an integrated model of PFM in Portugal, setting up a generic framework and developing aspects of integration, given the international good practices, and establishing the main guidelines for the operation of networks identified in the model. The proposed model aims to solve the weaknesses identified by integrating the functions of PFM: Accounting, Budget and Treasury. This means the use of accrual accounting based on a suitable chart of accounts by the fullness of government, the review of State Budget classifiers, the adoption of State Budget good governance and the development of a Treasury Single Account system, in the context of a modern cash management.