Improved approach for infrequent weighted itemsets in data mining

Pooja A. Keste, Nuzhat F. Shaikh
2016 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)  
Mining frequent items in data mining are useful for retrieving the related data present in the dataset. Using an input dataset the weighting function is calculated. Infrequent weighted itemset minimum support value is calculated. Using the minimum support value the infrequent weighted itemset support value is calculated. Then the summation is calculated for all the systems in separately. Then combine the two systems and summate the values which are minimum. Finally, three systems are combined
more » ... d summate the values which are minimum among the three. Find the threshold value for the dataset and filter the systems combination. If the summation value is greater than the threshold means, then the combination of systems are not considered. Otherwise, it is considered for the future result. Then find the equivalent weighted transaction dataset from transaction dataset. And find the infrequent weighted itemset minimum support value. Find the threshold value for the equivalent weighted itemset dataset. And get the satisfied system combinations. Then an infrequent weighted itemset miner is used to find the common systems that present in the two results. Here we can apply UP Growth algorithm to find the infrequent itemsets from the transactional database. By Using UP growth we can find the infrequent weighted itemset and the result is calculated. From the infrequent weighted itemset mining the final result is calculated.
doi:10.1109/iceeot.2016.7755177 fatcat:lirjwzekhvdr5kbk5ryydv2i2a