Informatics Publishing Limited

M. B. Hammawa ¹, G. Sampson ¹

Affiliations
1 Department of Computer Science, University of Abuja, Abuja, NG

Abstract

In this paper we considered several frameworks for data mining. These frameworks are based on different approaches, including inductive databases approach, the reductionist statistical approaches, data compression approach, constructive induction approach and some others. We considered advantages and limitations of these frameworks. We presented the view on data mining research as continuous and never- ending development process of an adaptive DM system towards the efficient utilization of available DM techniques for solving a current problem impacted by the dynamically changing environment. We discussed one of the traditional information systems frameworks and, drawing the analogy to this framework, we considered a data mining system as the special kind of adaptive information system. We adapted the information systems development framework for the context of data-mining systems development.

Keywords

Data Mining, Information Systems, Knowledge Discovery Databases.

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M. B. Hammawa ¹

Affiliations
1 Department of Computer Science, University of Abuja, Abuja, NG

Abstract

Currently, huge electronic data repositories are being maintained by banks and other financial institutions. Valuable bits of information are embedded in these data repositories. The huge size of these data sources make it impossible for a human analyst to come up with interesting information (or patterns) that will help in the decision making process. A number of commercial enterprises have been quick to recognize the value of this concept, as a consequence of which the software market itself for data mining is expected to be in excess of 10 billion USD. This note is intended for bankers, who would like to get aware of the possible applications of data mining to enhance the performance of some of their core business processes. In this note, the author discusses broad areas of application, like risk management, portfolio management, trading, customer profiling and customer care, where data mining techniques can be used in banks and other financial institutions to enhance their business performance.

Keywords

Data Mining, Banks, Financial Institutions, Risk Management, Portfolio Management, Trading, CRM, Customer Profiling.

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