Data Mining and Knowledge Discovery Handbook, 2 Edition part 69. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 660 Jean-Francois Boulicaut and Cyrille Masson Use database databasejiame Use hierarchy hierarchy .name For attribute Mine associations as pattern-name Matching metapattern From relation s Where condition Order by order Jist Group by grouping_list Having condition With interest-measure Threshold value OLE DB for DM OLE DB for DM has been designed by Microsoft Corporation Netz et al. 2000 . It is an extension of the OLE DB API to access database systems. More precisely it aims at supporting the communication between the data sources and the solvers that are not necessarily implemented inside the query evaluation system. It can thus work with many different solvers and types of patterns. To support the manipulation of the objects of the API during a KDD process OLE DB for DM proposes a language as an extension to SQL. The concept of OLE DB for DM relies on the definition of Data Mining Models DMM . object that correspond to extraction contexts in KDD. Indeed whereas the other language proposals made the assumption that the data almost have a suitable format for the extraction OLE DB for DM considers it is not always the case and let the user defines a virtual object that will have a suitable format for the extraction and that will be populated with the needed data. Once the extraction algorithm has been applied on this DMM the DMM will become an object containing patterns or models. It will then be possible to query this DMM as a rule base or to use it as a classifier. The global syntax for creating a DMM is the following CREATE MINING MODEL DMM name columns definition USING algorithm algorithm parameters For each column it is possible to specify the data type and if it is the target attribute of the model to be learnt in case of classification. Moreover a column can correspond to a nested table which is useful when populating the mining model with data taken in tables linked by a one-to-many relationship. For the moment OLE DB for DM is implemented in the
