Mining Highly Qualitative Multidimensional Association Rules

The tremendous growth in data has generated the need for new techniques that can intelligently transform the massive data into useful information and knowledge. Data Mining is such a technique that extracts non-trivial, implicit, previously unknown and potentially useful information from the data in databases. Association Rule Mining is one of the most important and well-researched techniques of data mining. It aims to extract interesting correlations, frequent patterns, associations of casual structures among sets of items in the transaction databases or other data repositories. Association rules are widely used in market databases, spatial databases, biological databases, medical databases and crime databases. This paper focuses a new algorithm to mine both positive and negative rules from the real-time surveyed medical database. Association rules are defined as implication of the form A->B where A and B are frequent itemsets in a transaction database. This new algorithm extends this definition to include association rules of forms A ->^B, ^A -> B and ^A -> ^B, which indicate negative associations between itemsets is called negative rules. Negative rules are generated from infrequent itemsets using multidimensional data model.