An important motivation for the development of inductive databases and query languages for data mining is that such an approach will increase the flexibility with which data mining can be performed. By integrating data mining more closely into a database querying framework, separate steps such as data preprocessing, data mining, and postprocessing of the results, can all be handled using one query language. In this chapter, we compare six existing data mining query languages, all extensions of the standard relational query language SQL, from this point of view: how flexible are they with respect to the tasks they can be used for, and how easily can those tasks be performed? We verify whether and how these languages can be used to perform four prototypical data mining tasks in the domain of itemset and association rule mining, and summarize their stronger and weaker points. Besides offering a comparative evaluation of different data mining query languages, this chapter also provides a motivation for a following chapter, where a deeper integration of data mining into databases is proposed, one that does not rely on the development of a new query language, but where the structure of the database itself is extended.
|Title of host publication||Inductive Databases and Constraint-Based Data Mining|
|Editors||S. Dzeroski, B. Goethals, P. Panov|
|Place of Publication||New York|
|Publication status||Published - 2010|