Introduction to Data, Text, and Web Mining for Business Analytics Minitrack

Abstract

Data mining (along with its derivatives that include text mining and Web mining) is one of the most popular enablers of business analytics. Although its roots dates back to late 1980s and early 1990s, most noteworthy/impactful outcomes of data mining come out after the turn of this century. Many believe that the recent popularity of analytics can largely be credited to the increasing use of data mining, which is capable of extracting and providing much needed insight and knowledge to decision makers at any and all levels of managerial hierarchy. The term data mining was originally used to describe the process through which previously unknown patterns in data were discovered. This definition has since been stretched beyond those limits by software vendors and consultancy companies to include most forms of data analysis in order to increase its reach and capability. With the emergence of analytics as an overarching term for all data analyses, data mining is put back into its proper place-a critical part of analytics continuum where the new discovery of knowledge happens. This mini-track has six papers, collectively illustrating the depth and breadth of data, text and Web mining, and their innovative applications to interesting and highly challenging business problems.