Data mining: an industrial research perspective

Abstract

Just what exactly is data mining? At a broad level, it is the process by which accurate and previously unknown information is extracted from large volumes of data. This information should be in a form that can be understood, acted upon, and used for improving decision processes. Obviously, with this definition, data mining encompasses a broad set of technologies, including data warehousing, database management, data analysis algorithms, and visualization. The crux of the appeal for this new technology lies in the data analysis algorithms, since they provide automated mechanisms for sifting through data and extracting useful information. The analysis capability of these algorithms, coupled with today's data warehousing and database management technology, make corporate and industrial data mining possible. The data representation model for such algorithms is quite straightforward. Data is considered to be a collection of records, where each record is a collection of fields. Using this tabular data model, data mining algorithms are designed to operate on the contents, under differing assumptions, and delivering results in differing formats. The data analysis algorithms (or data mining algorithms, as they are more popularly known nowadays) can be divided into three major categories based on the nature of their information extraction: predictive modeling (also called classification or supervised learning), clustering (also called segmentation or unsupervised learning), and frequent pattern extraction.