document: Rule interestingness analysis using OLAP operations The problem of interestingness of discovered rules has been investigated by many researchers . The issue is that data mining algorithms often generate too many rules , which make it very hard for the user to find the interesting ones . Over the years many techniques have been proposed . However , few have made it to real-life applications . Since August 2004 , we have been working on a major application for Motorola . The objective is to find causes of cellular phone call failures from a large amount of usage log data . Class association rules have been shown to be suitable for this type of diagnostic data mining application . We were also able to put several existing interestingness methods to the test , which revealed some major shortcomings . One of the main problems is that most existing methods treat rules individually . However , we discovered that users seldom regard a single rule to be interesting by itself . A rule is only interesting in the context of some other rules . Furthermore , in many cases , each individual rule may not be interesting , but a group of them together can represent an important piece of knowledge . This led us to discover a deficiency of the current rule mining paradigm . Using non-zero minimum support and non-zero minimum confidence eliminates a large amount of context information , which makes rule analysis difficult . This paper proposes a novel approach to deal with all of these issues , which casts rule analysis as OLAP operations and general impression mining . This approach enables the user to explore the knowledge space to find useful knowledge easily and systematically . It also provides a natural framework for visualization . As an evidence of its effectiveness , our system , called Opportunity Map , based on these ideas has been deployed , and it is in daily use in Motorola for finding actionable knowledge from its engineering and other types of data sets .
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