Data Mining - Mehmed Kantardzic [6]
Data mining is one of the fastest growing fields in the computer industry. Once a small interest area within computer science and statistics, it has quickly expanded into a field of its own. One of the greatest strengths of data mining is reflected in its wide range of methodologies and techniques that can be applied to a host of problem sets. Since data mining is a natural activity to be performed on large data sets, one of the largest target markets is the entire data-warehousing, data-mart, and decision-support community, encompassing professionals from such industries as retail, manufacturing, telecommunications, health care, insurance, and transportation. In the business community, data mining can be used to discover new purchasing trends, plan investment strategies, and detect unauthorized expenditures in the accounting system. It can improve marketing campaigns and the outcomes can be used to provide customers with more focused support and attention. Data-mining techniques can be applied to problems of business process reengineering, in which the goal is to understand interactions and relationships among business practices and organizations.
Many law enforcement and special investigative units, whose mission is to identify fraudulent activities and discover crime trends, have also used data mining successfully. For example, these methodologies can aid analysts in the identification of critical behavior patterns, in the communication interactions of narcotics organizations, the monetary transactions of money laundering and insider trading operations, the movements of serial killers, and the targeting of smugglers at border crossings. Data-mining techniques have also been employed by people in the intelligence community who maintain many large data sources as a part of the activities relating to matters of national security. Appendix B of the book gives a brief overview of the typical commercial applications of data-mining technology today. Despite a considerable level of overhype and strategic misuse, data mining has not only persevered but matured and adapted for practical use in the business world.
1.2 DATA-MINING ROOTS
Looking at how different authors describe data mining, it is clear that we are far from a universal agreement on the definition of data mining or even what constitutes data mining. Is data mining a form of statistics enriched with learning theory or is it a revolutionary new concept? In our view, most data-mining problems and corresponding solutions have roots in classical data analysis. Data mining has its origins in various disciplines, of which the two most important are statistics and machine learning. Statistics has its roots in mathematics; therefore, there has been an emphasis on mathematical rigor, a desire to establish that something is sensible on theoretical grounds before testing it in practice. In contrast, the machine-learning community has its origins very much in computer practice. This has led to a practical orientation, a willingness to test something out to see how well it performs, without waiting for a formal proof of effectiveness.
If the place given to mathematics and formalizations is one of the major differences between statistical and machine-learning approaches to data mining, another is the relative emphasis they give to models and algorithms. Modern statistics is almost entirely driven by the notion of a model. This is a postulated structure, or an approximation to a structure, which could