A modified multi-class association rule for text mining

Classification and association rule mining are significant tasks in data mining. Integrating association rule discovery and classification in data mining brings us an approach known as the associative classification. One common shortcoming of existing Association Classifiers is the huge number of rules produced in order to obtain high classification accuracy. This study proposes s a Modified Multi-class Association Rule Mining (mMCAR) that consists of three procedures; rule discovery, rule pruning and group-based class assignment. The rule discovery and rule pruning procedures are designed to reduce the number of classification rules. On the other hand, the group-based class assignment procedure contributes in improving the classification accuracy. Experiments on the structured and unstructured text datasets obtained from the UCI and Reuters repositories are performed in order to evaluate the proposed Association Classifier. The proposed mMCAR classifier is benchmarked against the traditional classifiers and existing Association Classifiers. Experimental results indicate that the proposed Association Classifier, mMCAR, produced high accuracy with a smaller number of classification rules. For the structured dataset, the mMCAR produces an average of 84.24% accuracy as compared to MCAR that obtains 84.23%. Even though the classification accuracy difference is small, the proposed mMCAR uses only 50 rules for the classification while its benchmark method involves 60 rules. On the other hand, mMCAR is at par with MCAR when unstructured dataset is utilized. Both classifiers produce 89% accuracy but mMCAR uses less number of rules for the classification. This study contributes to the text mining domain as automatic classification of huge and widely distributed textual data could facilitate the text representation and retrieval processes

MapReduce network enabled algorithms for classification based on association rules

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.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

Definition of Descriptive and Diagnostic Measurements for Model Fragment Retrieval

Tesis por compendio[ES] Hoy en día, el software existe en casi todo. Las empresas a menudo desarrollan y mantienen colecciones de sistemas de software personalizados que comparten algunas características entre ellos, pero que también tienen otras características particulares. Conforme el número de características y el número de variantes de un producto crece, el mantenimiento del software se vuelve cada vez más complejo. Para hacer frente a esta situación la Comunidad de Ingeniería del Software basada en Modelos está abordando una actividad clave: la Localización de Fragmentos de Modelo. Esta actividad consiste en la identificación de elementos del modelo que son relevantes para un requisito, una característica o un bug. Durante los últimos años se han propuesto muchos enfoques para abordar la identificación de los elementos del modelo que corresponden a una funcionalidad en particular. Sin embargo, existe una carencia a la hora de cómo se reportan las medidas del espacio de búsqueda, así como las medidas de la solución a encontrar. El objetivo de nuestra tesis radica en proporcionar a la comunidad dedicada a la actividad de localización de fragmentos de modelo una serie de medidas (tamaño, volumen, densidad, multiplicidad y dispersión) para reportar los problemas de localización de fragmentos de modelo. El uso de estas novedosas medidas ayuda a los investigadores durante la creación de nuevos enfoques, así como la mejora de aquellos enfoques ya existentes. Mediante el uso de dos casos de estudio reales e industriales, esta tesis pone en valor la importancia de estas medidas para comparar resultados de diferentes enfoques de una manera precisa. Los resultados de este trabajo han sido redactados y publicados en foros, conferencias y revistas especializadas en los temas y contexto de la investigación. Esta tesis se presenta como un compendio de artículos acorde a la regulación de la Universitat Politècnica de València. Este documento de tesis presenta los temas, el contexto y los objetivos de la investigación. Presenta las publicaciones académicas que se han publicado como resultado del trabajo y luego analiza los resultados de la investigación.[CA] Hui en dia, el programari existix en quasi tot. Les empreses sovint desenrotllen i mantenen col·leccions de sistemes de programari personalitzats que compartixen algunes característiques entre ells, però que també tenen altres característiques particulars. Conforme el nombre de característiques i el nombre de variants d'un producte creix, el manteniment del programari es torna cada vegada més complex. Per a fer front a esta situació la Comunitat d'Enginyeria del Programari basada en Models està abordant una activitat clau: la Localització de Fragments de Model. Esta activitat consistix en la identificació d'elements del model que són rellevants per a un requisit, una característica o un bug. Durant els últims anys s'han proposat molts enfocaments per a abordar la identificació dels elements del model que corresponen a una funcionalitat en particular. No obstant això, hi ha una carència a l'hora de com es reporten les mesures de l'espai de busca, així com les mesures de la solució a trobar. L'objectiu de la nostra tesi radica a proporcionar a la comunitat dedicada a l'activitat de localització de fragments de model una sèrie de mesures (grandària, volum, densitat, multiplicitat i dispersió) per a reportar els problemes de localització de fragments de model. L'ús d'estes noves mesures ajuda als investigadors durant la creació de nous enfocaments, així com la millora d'aquells enfocaments ja existents. Per mitjà de l'ús de dos casos d'estudi reals i industrials, esta tesi posa en valor la importància d'estes mesures per a comparar resultats de diferents enfocaments d'una manera precisa. Els resultats d'este treball han sigut redactats i publicats en fòrums, conferències i revistes especialitzades en els temes i context de la investigació. Esta tesi es presenta com un compendi d'articles d'acord amb la regulació de la Universitat Politècnica de València. Este document de tesi presenta els temes, el context i els objectius de la investigació. Presenta les publicacions acadèmiques que s'han publicat com resultat del treball i després analitza els resultats de la investigació.[EN] Nowadays, software exists in almost everything. Companies often develop and maintain a collection of custom-tailored software systems that share some common features but also support customer-specific ones. As the number of features and the number of product variants grows, software maintenance is becoming more and more complex. To keep pace with this situation, Model-Based Software Engineering Community is addressing a key-activity: Model Fragment Location (MFL). MFL aims at identifying model elements that are relevant to a requirement, feature, or bug. Many MFL approaches have been introduced in the last few years to address the identification of the model elements that correspond to a specific functionality. However, there is a lack of detail when the measurements about the search space (models) and the measurements about the solution to be found (model fragment) are reported. The goal of this thesis is to provide insights to MFL Research Community of how to improve the report of location problems. We propose using five measurements (size, volume, density, multiplicity, and dispersion) to report the location problems during MFL. The usage of these novel measurements support researchers during the creation of new MFL approaches and during the improvement of those existing ones. Using two different case studies, both real and industrial, we emphasize the importance of these measurements in order to compare results in a deeply way. The results of the research have been redacted and published in forums, conferences, and journals specialized in the topics and context of the research. This thesis is presented as compendium of articles according the regulations in Universitat Politècnica de València. This thesis document introduces the topics, context, and objectives of the research, presents the academic publications that have been published as a result of the work, and then discusses the outcomes of the investigation.Ballarin Naya, M. (2021). Definition of Descriptive and Diagnostic Measurements for Model Fragment Retrieval [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/171604TESISCompendi

A framework for trend mining with application to medical data

This thesis presents research work conducted in the field of knowledge discovery. It presents an integrated trend-mining framework and SOMA, which is the application of the trend-mining framework in diabetic retinopathy data. Trend mining is the process of identifying and analysing trends in the context of the variation of support of the association/classification rules that have been extracted from longitudinal datasets. The integrated framework concerns all major processes from data preparation to the extraction of knowledge. At the pre-process stage, data are cleaned, transformed if necessary, and sorted into time-stamped datasets using logic rules. At the next stage, time-stamp datasets are passed through the main processing, in which the ARM technique of matrix algorithm is applied to identify frequent rules with acceptable confidence. Mathematical conditions are applied to classify the sequences of support values into trends. Afterwards, interestingness criteria are applied to obtain interesting knowledge, and a visualization technique is proposed that maps how objects are moving from the previous to the next time stamp. A validation and verification (external and internal validation) framework is described that aims to ensure that the results at the intermediate stages of the framework are correct and that the framework as a whole can yield results that demonstrate causality. To evaluate the thesis, SOMA was developed. The dataset is, in itself, also of interest, as it is very noisy (in common with other similar medical datasets) and does not feature a clear association between specific time stamps and subsets of the data. The Royal Liverpool University Hospital has been a major centre for retinopathy research since 1991. Retinopathy is a generic term used to describe damage to the retina of the eye, which can, in the long term, lead to visual loss. Diabetic retinopathy is used to evaluate the framework, to determine whether SOMA can extract knowledge that is already known to the medics. The results show that those datasets can be used to extract knowledge that can show causality between patients’ characteristics such as the age of patient at diagnosis, type of diabetes, duration of diabetes, and diabetic retinopathy

Discovery and Effective Use of Frequent Item-set Mining and Association Rules in Datasets

The unprecedented rise in digitized data generation has led to the ever-expanding demand for sophisticated storage and analysis methods capable of handling vast amounts of complex data, much of which is stored within many databases. Owing to the large size of such databases, employment of sophisticated analysis methods, such as data mining and machine learning, becomes necessary to extract useful insights regarding a given system under study. Frequent itemset mining and association rules mining represent two key approaches to mining knowledge stored in databases. However, handling of large databases often leads to time-consuming calculations that necessitate large amounts of memory. In this regard, the development of methods capable of enabling faster, less laborious search or pattern discovery remains a central focus in the field of data mining. Incontestably, such methods could aid in faster processing and knowledge extraction, enabling new breakthroughs in how knowledge is acquired from data and applied in real-world applications. However, real-world applications are often hindered by limitations inherent to currently available algorithms. For instance, many itemset mining algorithms are known to first store a given database as a tree structure in memory. However, such algorithms fail to provide a tight upper bound on the number of nodes that will be generated during the tree building process accordingly, there are no upper bounds governing the amount of memory that is needed to generate such trees. As such, practical implementation of frequent itemset mining algorithms is often restricted by memory consumption. However, despite the importance of memory consumption in the applicability of itemset mining, this factor has not drawn adequate attention from the data mining community and remains as a key challenge in its application. In addition, the majority of algorithms widely used and studied to date are known to require multiple database scans, a factor which restricts their applicability for incremental mining applications. In this regard, the development of an algorithm capable of dynamically mining frequent patterns on-the-fly would open new pathways in data mining, enabling the application of itemset mining methods to new real-world applications, in addition to vastly improving current applications. In this thesis, different approaches are proposed in relation to the above-mentioned limitations currently hampering further progress in this significant area of data mining. First, an upper bound on the number of nodes of well-known tree structures in frequent itemset mining is presented. Second, aiming to overcome the memory consumption constraint, a memory-efficient method to store data processed by the frequent itemset mining algorithm is proposed, where instead of a tree, data is stored in a compact directed graph whose nodes represent items. Third, an algorithm is proposed to overcome costly databases scans in the form of a novel SPFP-tree (single pass frequent pattern tree) algorithm. Lastly, approaches that allow for frequent itemset and association rules to be practically and effectively used in real world applications are proposed. First, the quality and effectiveness of frequent itemset mining in solving a real world facility management problem is examined. Second, with aims of improving the quality of recommendations made to users, as well as to overcome the cold-start problem suffered by new users, a hybrid approach is herein proposed for the application of association rules into recommender systems

Machine Learning Approaches for Breast Cancer Survivability Prediction

Breast cancer is one of the leading causes of cancer death in women. If not diagnosed early, the 5-year survival rate of patients is just about 26\%. Furthermore, patients with similar phenotypes can respond differently to the same therapies, which means the therapies might not work well for some of them. Identifying biomarkers that can help predict a cancer class with high accuracy is at the heart of breast cancer studies because they are targets of the treatments and drug development. Genomics data have been shown to carry useful information for breast cancer diagnosis and prognosis, as well as uncovering the disease’s mechanism. Machine learning methods are powerful tools to find such information. Feature selection methods are often utilized in supervised learning and unsupervised learning tasks to deal with data containing a large number of features in which only a small portion of them are useful to the classification task. On the other hand, analyzing only one type of data, without reference to the existing knowledge about the disease and the therapies, might mislead the findings. Effective data integration approaches are necessary to uncover this complex disease. In this thesis, we apply and develop machine learning methods to identify meaningful biomarkers for breast cancer survivability prediction after a certain treatment. They include applying feature selection methods on gene-expression data to derived gene-signatures, where the initial genes are collected concerning the mechanism of some drugs used breast cancer therapies. We also propose a new feature selection method, named PAFS, and apply it to discover accurate biomarkers. In addition, it has been increasingly supported that, sub-network biomarkers are more robust and accurate than gene biomarkers. We proposed two network-based approaches to identify sub-network biomarkers for breast cancer survivability prediction after a treatment. They integrate gene-expression data with protein-protein interactions during the optimal sub-network searching process and use cancer-related genes and pathways to prioritize the extracted sub-networks. The sub-network search space is usually huge and many proteins interact with thousands of other proteins. Thus, we apply some heuristics to avoid generating and evaluating redundant sub-networks