Finding meaningful outliers by incorporating negative association rules in Frequent Pattern Outlier Detection Method

Outlier Mining has always attract much attention among the data mining community. This paper discusses on the discovery of meaningful outlier based on Frequent Pattern Outlier Detection Method. The PAR rules obtained is explored. By incorporating the Negative Association Rules to the PAR rules, a comprehensive and significant knowledge will be able to discover from the meaningful outliers. These would help experts in the field to interpret better for hidden knowledge especially in medical and scientific fields.