【正文】 a navigation applications, and the ability to store large databases is critical to data mining. From the user’s point of view, the four steps listed in Table 1 were revolutionary because they allowed new business questions to be answered accurately and quickly. Evolutionary Step Business Question Enabling Technologies Product Providers Characteristics Data Collection (1960s) What was my total revenue in the last five years? Computers, tapes, disks IBM, CDC Retrospective, static data delivery Data Access (1980s) What were unit sales in New England last March? Relational databases (RDBMS), Structured Query Language (SQL), ODBC Oracle, Sybase, Informix, IBM, Microsoft Retrospective, dynamic data delivery at record level Data Warehousing amp。t. For instance, if you were looking for a sunken Spanish galleon on the high seas the first thing you might do is to research the times when Spanish treasure had been found by others in the past. You might note that these ships often tend to be found off the coast of Bermuda and that there are certain characteristics to the ocean currents, and certain routes that have likely been taken by the ship’s captains in that era. You note these similarities and build a model that includes the characteristics that are mon to the locations of these sunken treasures. With these models in hand you sail off looking for treasure where your model indicates it most likely might be given a similar situation in the past. Hopefully, if you39。t know the long distance calling usage of these prospects (since they are most likely now customers of your petition). You39。 1). Sometimes called a knearest neighbor technique. nonlinear model An analytical model that does not assume linear relationships in the coefficients of the variables being studied. OLAP Online analytical processing. Refers to arrayoriented database applications that allow users to view, navigate through, manipulate, and analyze multidimensional databases. outlier A data item whose value falls outside the bounds enclosing most of the other corresponding values in the sample. May indicate anomalous data. Should be examined carefully。 Bradstreet can yield a prioritized list of prospects by region. ? A large consumer package goods pany can apply data mining to improve its sales process to retailers. Data from consumer panels, shipments, and petitor activity can be applied to understand the reasons for brand and store switching. Through this analysis, the manufacturer can select promotional strategies that best reach their target customer segments. Each of these examples have a clear mon ground. They leverage the knowledge about customers implicit in a data warehouse to reduce costs and improve the value of customer relationships. These anizations can now focus their efforts on the most important (profitable) customers and prospects, and design targeted marketing strategies to best reach them. Conclusion Comprehensive data warehouses that integrate operational data with customer, supplier, and market information have resulted in an explosion of information. Competition requires timely and sophisticated analysis on an integrated view of the data. However, there is a growing gap between more powerful storage and retrieval systems and the users’ ability to effectively analyze and act on the information they contain. Both relational and OLAP technologies have tremendous capabilities for navigating massive data warehouses, but brute force navigation of data is not enough. A new technological leap is needed to structure and prioritize information for specific enduser problems. The data mining tools can make this leap. Quantifiable business benefits have been proven through the integration of data mining with current information systems, and new products are 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 memb