Dissimilar Rule Mining and Ranking Technique for Associative Classification

[1] Using the new data mining techniques it can be discovered new patterns that offer new path analysis, new association rules, and new classification and clusters rules.. As we

This algorithm can extract the minimal generators and build a structure partially ordered called Frequent minimal generators lattice in order to perform a v ertical

Our research is a further step of the Tree Based Association Rule Mining (TBAR) algorithm, used in relational databases for finding the association rules .In

The proposed approach will make use of this modified ant colony algorithm to generate new association rules for the dynamic changing database which consists of set of

The work on association rules was extended from patterns [1,2,11] ,the authors explored data cube-based [2] rule mining algorithms on multidimensional databases, where

Since we are using generic product data we will compare mature and emerging market positive association rules results using the main indicators of the rules instead of the rule

All association rule mining algorithms like apriori algorithm and fp-growth algorithms are using mainly two steps in extracting the association rule. 1) Generation of

Generation of association rule was first introduced in [18] and AIS algorithm was proposed for mining of all types of association rules. An algorithm called SETM was proposed