An Enhanced and Reliable Efficient Algorithm for Mining Authentication Utility Item Sets

High utility itemsets (HUIs) mining is a developing theme in information mining, which alludes to finding all itemsets having an utility gathering a client determined least utility edge min_util. In any case, setting min_util fittingly is a troublesome issue for clients. As a rule, finding a suitable least utility limit by experimentation is a dreary procedure for clients. On the off chance that min_util is set too low, such a large number of HUIs will be created, which may cause the mining procedure to be exceptionally wasteful. Then again, if min_util is set too high, all things considered, no HUIs will be found. we propose a strategy and framework for confirming messages is given. A message validation framework creates irregular string and that will be sent to the beneficiary's portable and message sent to the beneficiary by means of mail at the opposite side the collector got the message through the mail and that will be in encoded shape and the beneficiary will decode the message with key that is sent to his versatile. The message validation framework at that point decides if the recovered message coordinates the first message. In the event that the codes coordinate, the uprightness and validness of the message are confirmed.

[1]  Hui Xiong,et al.  Mining strong affinity association patterns in data sets with skewed support distribution , 2003, Third IEEE International Conference on Data Mining.

[2]  Keun Ho Ryu,et al.  Discovering high utility itemsets with multiple minimum supports , 2014, Intell. Data Anal..

[3]  Unil Yun,et al.  Mining top-k frequent patterns with combination reducing techniques , 2013, Applied Intelligence.

[4]  Tzung-Pei Hong,et al.  Applying the maximum utility measure in high utility sequential pattern mining , 2014, Expert Syst. Appl..

[5]  Mengchi Liu,et al.  Mining high utility itemsets without candidate generation , 2012, CIKM.

[6]  Vincent S. Tseng,et al.  Mining High Utility Itemsets in Big Data , 2015, PAKDD.

[7]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[8]  Vincent S. Tseng,et al.  Novel Concise Representations of High Utility Itemsets Using Generator Patterns , 2014, ADMA.

[9]  Srikumar Krishnamoorthy,et al.  Pruning strategies for mining high utility itemsets , 2015, Expert Syst. Appl..

[10]  Philip S. Yu,et al.  Mining top-K high utility itemsets , 2012, KDD.

[11]  Jiawei Han,et al.  Mining top-k frequent closed patterns without minimum support , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[12]  Philip S. Yu,et al.  Mining high utility episodes in complex event sequences , 2013, KDD.

[13]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[14]  Benjamin C. M. Fung,et al.  Direct Discovery of High Utility Itemsets without Candidate Generation , 2012, 2012 IEEE 12th International Conference on Data Mining.

[15]  Ming-Syan Chen,et al.  Mining top-k frequent patterns in the presence of the memory constraint , 2008, The VLDB Journal.

[16]  Hui Xiong,et al.  TOP-COP: Mining TOP-K Strongly Correlated Pairs in Large Databases , 2006, Sixth International Conference on Data Mining (ICDM'06).

[17]  Tzung-Pei Hong,et al.  Efficient updating of discovered high-utility itemsets for transaction deletion in dynamic databases , 2015, Adv. Eng. Informatics.

[18]  Jiawei Han,et al.  TFP: an efficient algorithm for mining top-k frequent closed itemsets , 2005, IEEE Transactions on Knowledge and Data Engineering.

[19]  Vincent S. Tseng,et al.  Mining Top-K Association Rules , 2012, Canadian Conference on AI.

[20]  Philip S. Yu,et al.  Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets , 2011, 2011 IEEE 11th International Conference on Data Mining.

[21]  Chin-Chen Chang,et al.  Isolated items discarding strategy for discovering high utility itemsets , 2008, Data Knowl. Eng..

[22]  Philip S. Yu,et al.  UP-Growth: an efficient algorithm for high utility itemset mining , 2010, KDD.

[23]  Vincent S. Tseng,et al.  Mining Top-K Sequential Rules , 2011, ADMA.

[24]  Young-Koo Lee,et al.  Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases , 2009, IEEE Transactions on Knowledge and Data Engineering.

[25]  Jiawei Han,et al.  TSP: mining top-K closed sequential patterns , 2003, Third IEEE International Conference on Data Mining.

[26]  Unil Yun,et al.  A fast perturbation algorithm using tree structure for privacy preserving utility mining , 2015, Expert Syst. Appl..

[27]  Heungmo Ryang,et al.  Top-k high utility pattern mining with effective threshold raising strategies , 2015, Knowl. Based Syst..

[28]  Hui Xiong,et al.  Scaling up top-K cosine similarity search , 2011, Data Knowl. Eng..

[29]  Philip S. Yu,et al.  Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments , 2011, DASFAA.

[30]  Hui Xiong,et al.  Hyperclique pattern discovery , 2006, Data Mining and Knowledge Discovery.

[31]  Tran Minh Quang,et al.  ExMiner: An Efficient Algorithm for Mining Top-K Frequent Patterns , 2006, ADMA.

[32]  Philip S. Yu,et al.  Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets , 2015, IEEE Transactions on Knowledge and Data Engineering.

[33]  A. Choudhary,et al.  A fast high utility itemsets mining algorithm , 2005, UBDM '05.

[34]  Qiang Yang,et al.  Mining high utility itemsets , 2003, Third IEEE International Conference on Data Mining.

[35]  Heungmo Ryang,et al.  Incremental high utility pattern mining with static and dynamic databases , 2014, Applied Intelligence.