Variable Support Based Association Rule Mining

Analysing datasets requires sophisticated techniques which can help to unearth interesting patterns. One approach is to mine multidimensional association rules from data. The traditional association rule mining relies on uniform support and confidence values which does not always yield interesting rules due to varied nature of data. This paper presents a novel approach to mine multidimensional association rules from dataset with varying support. The improved algorithm is being proposed to overcome missing aspect of tradition rule mining algorithms like Apriori.