Theoretical Considerations for Data Mining

Data mining is a relatively new field, which developed during the 1990s and coalesced into a field of its own during the early years of the twenty-first century. It represents a confluence of several well-established fields of interest, namely, traditional statistical analysis, artificial intelligence, machine learning, and development of large databases. The modern Knowledge Discovery in Databases (KDD) process combines the mathematics used to discover interesting patterns in data with the entire process of extracting data and using resulting models to apply to other data sets to leverage the information for some purpose. This process blends business systems engineering, elegant statistical methods, and industrial-strength computing power to find structure. Data mining can provide a more complete understanding of data by finding patterns previously not seen and make models that predict, thus enabling people to make better decisions, take action, and therefore mold future events. Data mining tools must have the ability to process data input from very different database structures. In tools with graphical user interfaces (GUIs), multiple nodes must be configured to input data from very different data structures.