Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base
AuthorSowan, Bilal I.
SupervisorDahal, Keshav P.
Hossain, M. Alamgir
Fuzzy associative rule mining
Associative classification rule base
Decision support system
Minimizing prediction error
The University of Bradford theses are licenced under a Creative Commons Licence.
InstitutionUniversity of Bradford
DepartmentSchool of Computing, Informatics & Media
MetadataShow full item record
AbstractBuilding 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.
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The use of solubility parameters to predict the behaviour of a co-crystalline drug dispersed in a polymeric vehicle. Approaches to the prediction of the interactions of co-crystals and their components with hypromellose acetate succinate and the characterization of that interaction using crystallographic, microscopic, thermal, and vibrational analysis.Forbes, Robert T.; Bonner, Michael C.; Isreb, Abdullah (University of BradfordThe School of Pharmacy., 2013-04-09)Dispersing co-crystals in a polymeric carrier may improve their physicochemical properties such as dissolution rate and solubility. Additionally co-crystal stability may be enhanced. However, such dispersions have been little investigated to date. This study focuses on the feasibility of dispersing co-crystals in a polymeric carrier and theoretical calculations to predict their stability. Acetone/chloroform, ethanol/water, and acetonitrile were used to load and grow co-crystals in a HPMCAS film. Caffeine-malonic acid and ibuprofennicotinamide co-crystals were prepared using solvent evaporation method. The interactions between each of the co-crystals components and their mixtures with the polymer were studied. A solvent evaporation approach was used to incorporate each compound, a mixture, and co-crystals into HPMCAS films. Differential scanning calorimetry data revealed a higher affinity of the polymer to acidic compounds than their basic counterparts as noticed by the depression of the glass transition temperature (Tg). Moreover, the same drug loading produced films with different Tgs when different solvents were used. Solubility parameter values (SP) of the solvents were employed to predict that effect on the depression of polymer Tg with relative success. SP values were more successful in predicting the preferential affinity of two acidic compounds to interact with the polymer. This was confirmed using binary mixtures of naproxen, flurbiprofen, malonic acid, and ibuprofen. On the other hand, dispersing basic compounds such as caffeine or nicotinamide with malonic acid in HPMCAS film revealed the growth of co-crystals. A dissolution study showed that the average release of caffeine from films containing caffeine-malonic acid was not significantly different to that of films containing similar caffeine concentration. The stability of the caffeine-malonic acid co-crystals in HPMC-AS was prolonged to 8 weeks at 95% relative humidity and 45°C. The theory developed in this project, that an acidic drug with a SP value closer to the polymer will dominate the interaction process and prevent the majority of the other material from interacting with the polymer, may have utility in designing co-crystal systems in polymeric vehicles
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