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
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Bilal Sowan PhD thesis (final version).pdf (2.349Mb)
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Publication date
2012-02-13Supervisor
Dahal, Keshav P.Hossain, M. Alamgir
Keyword
PredictionFuzzy associative rule mining
Fuzzy clustering
Associative classification rule base
Data mining
Predictive model
Decision support system
Minimizing prediction error
Prediction performance
Institution
University of BradfordDepartment
School of Computing, Informatics & MediaAwarded
2011Metadata
Show full item recordAbstract
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.Type
ThesisQualification name
PhDCollections
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Rationalization of Racemate Resolution: Predicting Spontaneous Resolution through Crystal Structure Prediction.(2009-07-14)Crystal structure prediction simulations are reported on 5-hydroxymethyl-2-oxazolidinone and 4-hydroxymethyl-2-oxazolidinone to establish the feasibility of predicting the spontaneous resolution of racemates of small organic molecules. It is assumed that spontaneous resolution occurs when the enantiomorph is more stable than the racemic solid. The starting point is a gas phase conformational search to locate all low-energy conformations. These conformations are used to predict the possible crystal structures of 5- and 4-hydroxymethyl-2-oxazolidinone. In both cases, the racemic crystal structure is predicted to have the lowest energy. The energy differences between the lowest-energy racemic solids and the lowest-energy enantiomorphs are 0.2 kcal mol-1 for 5-hydroxymethyl-2-oxazolidinone and 0.9 kcal mol-1 for 4-hydroxymethyl-2-oxazolidinone. In the case of 4-hydroxymethyl-2-oxazolidinone, where the racemic crystal is known to be more stable and the experimental crystal structures of both the racemate and the enantiomorph are available, the simulation results match the observed data. For 5-hydroxymethyl-2-oxazolidinone, where only enantiopure crystals are observed experimentally, the known experimental structure is found 1.6 kcal mol-1 above the lowest-energy predicted structure. This work shows that it is possible to predict whether the racemate of a small chiral molecule can be resolved spontaneously, although further advances in the accuracy of lattice energy calculations are required.
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Predicting spontaneous racemate resolution using recent developments in crystal structure prediction(2009)A hybrid molecular mechanics and quantum mechanics solid state DFT method is used to re-rank the stability of racemic and enantiopure crystal structures of four molecules; 4-hydroxymethyl-2-oxazolidinone, 5-hydroxymethyl-2-oxazolidinone, 2-(4-hydroxyphenyl)-2,5,5-trimethylpyrrolidine-1-oxy and 2-(3-hydroxyphenyl)-2,5,5-trimethylpyrrolidine-1-oxy. Previous work using a force field based method to predict these crystal structures indicated that the lattice energy may be a suitable criterion for predicting whether a chiral molecule will resolve spontaneously on crystallisation. However, in some cases, the method had predicted an unrealistically high lattice energy for the structure corresponding to the experimentally observed one. The Hybrid DFT method successfully predicts those molecules which resolve spontaneously and furthermore predicts satisfactory lattice energies for all experimentally observed structures. Based on a comparison of the predicted lattice energies from the two methods it is concluded that the force fields used were not sufficiently accurate to predict spontaneous resolution with any confidence. However, the Hybrid DFT method is shown to be sufficiently accurate for making such predictions.
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Enhanced flare prediction by advanced feature extraction from solar images : developing automated imaging and machine learning techniques for processing solar images and extracting features from active regions to enable the efficient prediction of solar flares.(University of BradfordSchool of Computing, Informatics & Media, 2012-04-16)Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART¿s active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.
