【正文】 on the horizon that will bring this integration to an even wider audience of users. 1 META Group Application Development Strategies: Data Mining for Data Warehouses: Uncovering Hidden Patterns., 7/13/95 . 2 Gartner Group Advanced Technologies and Applications Research Note, 2/1/95. 3 Gartner Group High Performance Computing Research Note, 1/31/95. Glossary of Data Mining Terms analytical model A structure and process for analyzing a dataset. For example, a decision tree is a model for the classification of a dataset. anomalous data Data that result from errors (for example, data entry keying errors) or that represent unusual events. Anomalous data should be examined carefully because it may carry important information. artificial neural works Nonlinear predictive models that learn through training and resemble biological neural works in structure. CART Classification and Regression Trees. A decision tree technique used for classification of a dataset. Provides a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given oute. Segments a dataset by creating 2way splits. Requires less data preparation than CHAID. CHAID Chi Square Automatic Interaction Detection. A decision tree technique used for classification of a dataset. Provides a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given oute. Segments a dataset by using chi square tests to create multiway splits. Preceded, and requires more data preparation than, CART. classification The process of dividing a dataset into mutually exclusive groups such that the members of each group are as close as possible to one another, and different groups are as far as possible from one another, where distance is measured with respect to specific variable(s) you are trying to predict. For example, a typical classification problem is to divide a database of panies into groups that are as homogeneous as possible with respect to a creditworthiness variable with values Good and Bad. clustering The process of dividing a dataset into mutually exclusive groups such that the members of each group are as close as possible to one another, and different groups are as far as possible from one another, where distance is measured with respect to all available variables. data cleansing The process of ensuring that all values in a dataset are consistent and correctly recorded. data mining The extraction of hidden predictive information from large databases. data navigation The process of viewing different dimensions, slices, and levels of detail of a multidimensional database. See OLAP. data visualization The visual interpretation of plex relationships in multidimensional data. data warehouse A system for storing and delivering massive quantities of data. decision tree A treeshaped structure that represents a set of decisions. These decisions generate rules for the classification of a dataset. See CART and CHAID. dimension In a flat or relational database, each field in a record represents a dimension. In a multidimensional database, a dimension is a set of similar entities。 1). Sometimes called the knearest neighbor technique. ? Rule induction: The extraction of useful ifthen rules from data based on statistical significance. Many of these technologies have been in use for more than a decade in specialized analysis tools that work with relatively small volumes of data. These capabilities are now evolving to integrate directly with industrystandard data warehouse and OLAP platforms. The appendix to this white paper provides a glossary of data mining terms. How Data Mining Works How exactly is data mining able to tell you important things that you didn39。ve got a good model, you find your treasure. This act of model building is thus something that people have been doing for a long time, certainly before the advent of puters or data mining technology. What happens on puters, however, is not much different than the way people build models. Computers are loaded up with lots of information about a variety of situations where an answer is known and then the data mining software on the puter must run through that data and distill the characteristics of the data that should go into the model. Once the model is built it can then be used in similar situations where you don39。 may carry important information. parallel processing The coordinated use of multiple processors to perform putational tasks. Parallel processing can occur on a multiprocessor puter or on a work of workstations or PCs. predictive model A structure and process for predicting the values of specified variables in a dataset. prospective data analysis Data analysis that predicts future trends, behaviors, or events based on historical data. RAID Redundant Array of Inexpensive Disks. A technology for the efficient parallel storage of data for highperformance puter systems. retrospective data analysis Data analysis that provides insights into trends, behaviors, or events that have already occurred. rule induction The extraction of useful ifthen rules from data based on statistical significance. SMP Symmetric multiprocessor. A type of multiprocessor puter in which memory is shared among the processors. terabyte One trillion bytes. time series analysis The analysis of a sequence of measurements made at specified time intervals. Time is usually the dominating dimension of the data.