• K RAJA RAJESWARI
• DDD SURIBABU

High utility item sets(HUIs) mining is anemerging topic in datamining, which refers to discovering all itemsets having autility meeting a user specified minimum utility threshold min_util. However, setting min_util appropriately is a difficult problem for users.Generally speaking, finding an appropriate minimum utility threshold by trial and errorisatedious process for users. If min_util is set to olow,too many HUIs will be generated, which may cause the mining process to be very inefficient. On the otherhand, if min_util is set to ohigh,it is likely that no HUIs will be found. In this paper, we address the above issues by proposing a new framework for top-k high utility item set mining, where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU(miningTop-KUtilityitemsets) and TKO(miningTop-K utility itemsets in Onephase) are proposed formining such itemsets with out the need to setmin_util. We provide a structural comparison of the two algorithms with discussions on their advantages and limitations. Empirical evaluations on both realand synthetic datasets show that the performance of the proposed algorithms is close to that of the optimal case of state-of-the-art utility mining algorithms.

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"High Utility Item sets From Transactional Databases with efficient Algorithms utility pattern growth (UP-Growth) and UP-Growth+", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.471-474, June-2018, Available :http://www.jetir.org/papers/JETIR1806760.pdf

"High Utility Item sets From Transactional Databases with efficient Algorithms utility pattern growth (UP-Growth) and UP-Growth+", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp471-474, June-2018, Available at : http://www.jetir.org/papers/JETIR1806760.pdf