Knowledge Discovery Through Experiential Learning From Business and Other Contemporary Data Sources: A Review and Reappraisal

Abstract

Every day massive amount of data is generated, collected, and stored in information repositories such as databases and data warehouses. Current information technology is sufficiently mature and powerful to store any amount of raw data in an organized manner. However, finding useful patterns, trends, rules, correlations, and deviations in large amount of data, and/or making meaningful predictions from it still remains one of the main challenges of the information era. The more data one has, the more difficult it is to analyze and draw meaningful conclusions. Knowledge discovery in databases (KDD) and data mining (DM) is a field, which uses computer-based and analytic technologies to efficiently extract intelligence from data that humans need. In this article, we review the process of knowledge discovery in databases, and describe selected methodologies, methods and tools, tasks, basic learning paradigms, and applications for knowledge generation by computer learning from data instances. We also examine the current trends in the field with respect to the data types mined, data mining methods used, classes of data mining applications, as well as the data mining software used.