Affiliations: [a] Acharya Nagarjuna University, Andhra Pradesh, India | [b] Department of IT, GMRIT, Rajam, Srikakulam, Andhra Pradesh, India | [c] Department of CSE, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India
Abstract: High Utility Itemset Mining (HUIM) is playing an important role in extracting meaningful knowledge that is profitable itemsets rather than their occurrence frequency. HUIM result thrives into a huge number of utility itemsets that makes it difficult in decision policy. The alternative procedure is condensed representation of HUI’s that contains utility itemsets without any redundancy. Closed High Utility Itemsets (CHUI) and High Utility Generators (HUG) are lossless utility itemsets useful in recommendation systems. In this paper, we address how to extract Closed and Generator HUI’s from the given dataset. For faster accessing, it is proposed hash based technique that store itemsets with the frequency, utility value, closed flag, and generator flag. Experimental results shows that it outperforms other approaches in time and space.
Keywords: HUI’s, CHUIM, generator, hash, data mining
