Data Mining Applications: Promise and Challenges

Abstract

Data mining is an emerging field gaining acceptance in research and industry. This is evidenced by an increasing number of research publications, conferences, journals and industry initiatives focused in this field in the recent past. Data mining aims to solve an intricate problem faced by a number of application domains today with the deluge of data that exists and is continually collected, typically, in large electronic databases. That is, to extract useful, meaningful knowledge from these vast data sets. Human analytical capabilities are limited, especially in its ability to analyse large and complex data sets. Data mining provides a number of tools and techniques that enables analysis of such data sets. Data mining incorporates techniques from a number of fields including statistics, machine learning, database management, artificial intelligence, pattern recognition, and data visualisation. A number of definitions for data mining are presented in literature. Some of them are listed below: • “Data mining is the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Gartner Group, 1995). • “Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner” (Hand et al., 2001). • “Data mining is an interdisciplinary field bringing together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large data bases” (Cabena et al., 1998). • “The extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data” (Han & Kamber, 2001). We present application of data mining (also known as “Data Mining Applications”) as an “experiment” carried out using data mining techniques that result in gaining useful knowledge and insights pertaining to the application domain. Figure 1 below depicts this process.