Mining Generalized Association Rules and Sequential Patterns Using SQL Queries

Shiby Thomas, University of Florida and Sunita Sarawagi, IBM Almaden Research Center

Database integration of mining is becoming increasingly important with the installation of larger and larger data warehouses built around relational database technology. Most of the commercially available mining systems integrate loosely (typically, through an ODBC or SQL cursor interface) with data stored in DBMSs. In cases where the mining algorithm makes multiple passes over the data, it is also possible to cache the data in flat files rather than retrieve multiple times from the DBMS, to achieve better performance. Recent studies have found that for association rule mining, with carefully tuned SQL formulations it is possible to achieve performance comparable to systems that cache the data in files outside the DBMS. The SQL implementation has potential for offering other qualitative advantages like automatic parallelization, development ease, portability and inter-operability with relational operators. In this paper, we present several alternatives for formulating as SQL queries association rule generalized to handle items with hierarchies on them and sequential pattern mining. This work illustrates that it is possible to express computations that are significantly more complicated than simple boolean associations, in SQL using essentially the same framework.


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