Motivation: In the Sub-Saharan Africa countries there are several factors contributing to the collapse of firms. Most firms have failed due to poor corporate governance practices. The recent collapse of some firms in the financial and non-financial sectors in the Sub-region shows that there are challenges hindering effective corporate governance of firms in the Subregion. Consequently, this study uses empirical evidence to identify views about the important components of good corporate governance practice for listed firms: institutional characteristics; the board of directors; and the effects of external factors. Research question: The pertinent research question that this study addresses is the identification of the components that are essential for good corporate governance of firms in the Sub-region. This study tries to prioritise the components. Methodology: Data were collected by questionnaire administered to stakeholders of corporate governance of listed firms in Ghana, Nigeria and South Africa. Regression is used to estimate the relationship between institutional characteristic, responsibilities of the board of directors and external factors on corporate governance system. Main findings: 1. Enforcement, disclosure, transparency and regulatory frameworks may be necessary to improve corporate governance practice in all the countries in the Sub-region (SSAA). 2. There is evidence that commitment of board members to disclosure and communication may provide effective corporate governance practice. 3. Board duality (separation of role between chairman and CEO) is likely to hinder corporate governance practices. 4. We found that in all the countries in the Sub-region accounting system plays a major role to promote sound corporate governance practice. However, the political environment, societal and cultural factor, corruption, and economic factors such as macro-economic policies may hinder corporate governance practices.Policy recommendations: This study recommends that corporate governance stakeholders should adopt a whistle blowing method and also that institutional bodies should be more prudent in monitoring of rules and laws with stringent penalties. In addition, there should be adequate information and disclosure on the rights and obligation of the shareholder of firms in the sub-region region. There is need to increase the number and role of independent directors, increase the use of advisory vote by shareholders on executive compensation and facilitation of shareholders activism. Furthermore, there is a need to have autonomous regulatory bodies and supervisory agencies free from any political/ government interference in the implementation of the Code and Guideline of corporate governance. The regulatory bodies and the supervisory agencies should be manned or be under the leadership of people of goodwill, good character and trust. The Code or Guideline of corporate governance of Sub-Saharan Africa Anglophone countries should take cognisance of and be aligned with socio-cultural environment of the countries in the Sub-region.

Data mining is about the search for relationships and global patterns in large databases that are increasing in size. Data mining is beneficial for anyone who has a huge amount of data, for example, customer and business data, transaction, marketing, financial, manufacturing and web data etc. The results of data mining are also referred to as knowledge in the form of rules, regularities and constraints. Rule mining is one of the popular data mining methods since rules provide concise statements of potentially important information that is easily understood by end users and also actionable patterns. At present rule mining has received a good deal of attention and enthusiasm from data mining researchers since rule mining is capable of solving many data mining problems such as classification, association, customer profiling, summarization, segmentation and many others. This thesis makes several contributions by proposing rule mining methods using genetic algorithms and neural networks. The thesis first proposes rule mining methods using a genetic algorithm. These methods are based on an integrated framework but capable of mining three major classes of rules. Moreover, the rule mining processes in these methods are controlled by tuning of two data mining measures such as support and confidence. The thesis shows how to build data mining predictive models using the resultant rules of the proposed methods. Another key contribution of the thesis is the proposal of rule mining methods using supervised neural networks. The thesis mathematically analyses the Widrow-Hoff learning algorithm of a single-layered neural network, which results in a foundation for rule mining algorithms using single-layered neural networks. Three rule mining algorithms using single-layered neural networks are proposed for the three major classes of rules on the basis of the proposed theorems. The thesis also looks at the problem of rule mining where user guidance is absent. The thesis proposes a guided rule mining system to overcome this problem. The thesis extends this work further by comparing the performance of the algorithm used in the proposed guided rule mining system with Apriori data mining algorithm. Finally, the thesis studies the Kohonen self-organization map as an unsupervised neural network for rule mining algorithms. Two approaches are adopted based on the way of self-organization maps applied in rule mining models. In the first approach, self-organization map is used for clustering, which provides class information to the rule mining process. In the second approach, automated rule mining takes the place of trained neurons as it grows in a hierarchical structure.