Data mining is categorized as either Descriptive or Predictive. Descriptive data mining is to search massive data sets and discover the locations of unexpected structures or relationships, patterns, trends, clusters, and outliers in the data. On the other hand, Predictive is to build models and procedures for regression, classification, pattern recognition, or machine learning tasks, and assess the predictive accuracy of those models and procedures when applied to fresh data.
The mechanism used to search for patterns or structure in high-dimensional data might be manual or automated; searching might require interactively querying a database management system, or it might entail using visualization software to spot anomalies in the data. In machine-learning terms, descriptive data mining is known as unsupervised learning, whereas predictive data mining is known as supervised learning.
Most of the methods used in data mining are related to methods developed in statistics and machine learning. Foremost among those methods are the general topics of regression, classification, clustering, and visualization. Because of the enormous sizes of the data sets, many applications of data mining focus on dimensionality-reduction techniques (e.g., variable selection) and situations in which high-dimensional data are suspected of lying
on lower-dimensional hyperplanes. Recent attention has been directed to methods of identifying high-dimensional data lying on nonlinear surfaces or manifolds.
There are also situations in data mining when statistical inference — in its classical sense — either has no meaning or is of dubious validity: the former occurs when we have the entire population to search for answers, and the latter occurs when a data set is a “convenience” sample rather than being a random sample drawn from some large population. When data are collected through time (e.g., retail transactions, stock-market transactions, patient records, weather records), sampling also may not make sense; the time-ordering of the observations is crucial to understanding the phenomenon generating the data, and to treat the observations as independent when they may be highly correlated will provide biased results.
The central components of data mining are — in addition to statistical theory and methods
— computing and computational efficiency, automatic data processing, dynamic and interactive data visualization techniques, and algorithm development.
One of the most important issues in data mining is the computational problem of scalability. Algorithms developed for computing standard exploratory and confirmatory statistical methods were designed to be fast and computationally efficient when applied to small and medium-sized data sets; yet, it has been shown that most of these algorithms are not up to the challenge of handling huge data sets. As data sets grow, many existing
algorithms demonstrate a tendency to slow down dramatically (or even grind to a halt).