Data Mining and Knowledge Discovery: An Analytical Investigation

摘要

In recent years, the exponentially growing amount of data made traditional data analysis methods impractical. Knowledge discovery in databases (KDD) provides a framework for alternative methods that address this problem. In this research we follow the KDD process, develop a mathematical model of transforming data and information into knowledge and create a clustering data mining algorithm. To that end, we employ ideas from related, applicable fields (e.g., Operations Research, Inventory Management, and Information Theory). Consequently, we show the merit and value of applying a well-structured model to knowledge acquisition.