Article: High performance framework for mining association rules from hierarchical data cubes Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2015 Vol.10 No.3 pp.233 - 252 Abstract: Online analytical processing (OLAP) is considered as a database prototype that gives a platform for the rich analysis of multidimensional data. A logical data structure known as the data cube often supports the OLAP. However, mining association rules from multidimensional data using OLAP techniques with data mining facilities is an issue of substantial complexity. In practice, the complexity is excited by the existence of dimension hierarchies that subdivide dimensions into aggregation layers of various granularity. Discovery of hierarchy-sensitive association rules can be very costly on large cubes. In this paper, we present an OLAP hierarchy-sensitive framework that supports the efficient and transparent manipulation of dimension hierarchies for extracting association rules from data cube. The experimental results show that, when compared to the alternatives, very slight overhead is required to handle streams of inter-dimensional association rules requests. Inderscience Publishers - linking academia, business and industry through research

Title: High performance framework for mining association rules from hierarchical data cubes

Authors: Ahmad M. Taleb; Asadullah Shaikh; Todd Eavis; Nasser M. Taleb

Addresses: College of Computer Science and Information Systems, Najran University, Saudi Arabia ' College of Computer Science and Information Systems, Najran University, Saudi Arabia; Faculty of Computer Science and Information Technology, Institute of Business and Technology, Karachi, Pakistan ' Department of Computer Science, Concordia University, Montreal, Canada ' College of Business Administration, Al Ain University of Science and Technology, Al Ain, UAE

Abstract: Online analytical processing (OLAP) is considered as a database prototype that gives a platform for the rich analysis of multidimensional data. A logical data structure known as the data cube often supports the OLAP. However, mining association rules from multidimensional data using OLAP techniques with data mining facilities is an issue of substantial complexity. In practice, the complexity is excited by the existence of dimension hierarchies that subdivide dimensions into aggregation layers of various granularity. Discovery of hierarchy-sensitive association rules can be very costly on large cubes. In this paper, we present an OLAP hierarchy-sensitive framework that supports the efficient and transparent manipulation of dimension hierarchies for extracting association rules from data cube. The experimental results show that, when compared to the alternatives, very slight overhead is required to handle streams of inter-dimensional association rules requests.

Keywords: data mining; association rules mining; data cubes; dimension hierarchies; online analytical processing; OLAP; granularity.

DOI: 10.1504/IJBIDM.2015.071324

International Journal of Business Intelligence and Data Mining, 2015 Vol.10 No.3, pp.233 - 252

Received: 07 Jan 2015
Accepted: 07 Jan 2015
Published online: 20 Aug 2015 *

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Title: High performance framework for mining association rules from hierarchical data cubes

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