Mining Rare Patterns by Using Automated Threshold Support

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Essentially the most primary and crucial part of data mining is pattern mining. For acquiring important corre-lations among the information, method called itemset mining plays vital role Earlier, the notion of itemset mining was used to acquire the absolute most often occurring items in the itemset. In some situation, though having utility value less than threshold it is necessary to locate such items because they are of great use. Considering the thought of weight for each and every apparent items brings effectiveness for mining the pattern efficiently. Different mining algorithms are utilized to obtain the correlations among the information items based on frequency with the items in the dataset occurs. In frequent itemset, those things which occurs frequently whereas, in infrequent itemset the items that occur very rarely are obtained. Determining such form of data is tougher than to locate data which occurs frequently. Frequent Itemset Mining (FISM) locates large and frequent itemsets in huge data for example market baskets. Such data has two properties that are not addressed by FISM; Mixture property and projection property. Here the proposed system combines both mixture as well as projection property further providing automated support thresholds.

     

     


  • Keywords


    Infrequent itemset mining, minimum support, pattern mining, automated support threshold.

  • References


      [1] C. Sweetlin Hemalatha, V. Vaidehi,and R. Lakshmi, ”Minimal infer

      quent pattern based approach for mining outliers in data streams”,

      Jour nal on Expert Systems with Applications, Elsevier , 2014.

      [2] Mehdi Adda , Lei Wu , Sharon White, and Yi Feng, ”Pattern De

      tection withRare Itemset Mining, International Journal on Soft C

      puting, Artificial Intelligence and Applications (IJSCAI), Vol.1,

      No.1, August 2012.

      [3] Anu Augustin,Vince Paul and Vishnu G. Nair, ”High Utility Itemset Mining withTop-k CHUD (TCHUD) Algorithm”, International Journal of Computer Applications, 3 May 2017

      [4] Cheng-HsiungWeng, High Utility Itemset Mining withTop-k CHUD (TCHUD) Algorithm, Elsevier Journal 13 February 2011.

      [5] Hao Ying, John Tran, Peter Dews,Ayman Mansour, and R. Michael Massanari, ”A Method for Mining Infrequent Causal Associations and Its Application in Finding Adverse Drug Reaction Signal Pairs”, IEEE Transaction,4 April,2013

      [6] Jiaqi Zhu, Yunkun Wu, Zhongyi Hu, and Hongan Wang, ”WangMining User-Aware Rare Sequential Topic Patterns in Document Streams”, IEEE Transaction,2016.

      [7] Jennifer Lavergne, Ryan Benton and Vijay V. Raghavan , ”TRARM-RelSup: Targeted Rare Association Rule Mining Using Itemset Trees and the Relative Support Measure”, Springer, 2012.

      [8] C. Luca Cagliero and Paolo Garza, ”Infrequent Weighted Itemset Mining Using Frequent Pattern Growth”, IEEE Transaction on Knowledge and Data Engineering ,4, APRIL 2014 Busan, Korea.

      [9] A. Jalpa A Varsur1, Nikul G Virpariya , ”Mining Rare Itemset Based on FP Growth Algorithm”, International Conference

      [10] Wensheng Gan, Jerry Chun-Wei Lina, Philippe Fournier-Viger, Han-Chieh Chaoa,c, Justin Zhan ”Mining of frequent patterns with multiple minimum supports”,Elsevier,2017

      [11] Yun Sing Koh and Sri Devi Ravana, ”Unsupervised Rare Pattern Mining: A Survey”, ACM Transactions on Knowledge Discovery from Data, 2016.

      [12] Saeed Piri, Dursun Delen, Tieming Liu, William Paiva, Development of a New Metric to Identify Rare Patterns in Association Analysis:The Case of Analyzing Diabetes Complications ,10.1016/j.eswa.2017.09.061

      [13] Timothy M. Hospedales, Shaogang Gong, and Tao Xiang, ”Finding Rare Classes: Active Learning with Generative and Discriminative Models”, IEEE Transaction, 2013.

      [14] Jayakrushna Sahoo1Ashok Kumar Das, A. Goswami1, ”An efficient fast algorithm for discovering closed high utility itemsets”

      [15] Ashish Gupta, Akshay Mittal, Arnab Bhattacharya, ”Minimally Infre-quent Itemset Mining using Pattern-Growth Paradigm and Residual Trees, 17th International Conference on Management of Data ,2011

      [16] Varsur Jalpa A.,Desai Sonali P., Hathi Karishma B, ”Performance Analysis of Rare Itemset Mining Algorithms”,Journal of Emerging Technologies and Innovative Research (JETIR),2015.

      [17] Weimin Ouyang, Mining Rare Sequential Patterns in Data Streams with a Sliding Window ,The 2016 3rd International Conference on Systems and Informatics (ICSAI 2016).

      [18] Fernando Benites, Elena sapozhnikova, ”Evaluation of Hierarchical Interestingness measures for mining pairwise generalized association rules”, IEEE Transaction, 2014

      [19] Kantarcioglu, Chris Clifton, ”Privacy Preserving distributed Mining of Association Rules on Horizontally partitioned Data”,IEEE Transaction, 2004

      [20] Thiago Henrique Cupertino, Murillo Guimares Carneiro, Qiusheng Zheng, Junbao Zhang, Liang Zhao, ”A Scheme for High Level Data Classification Using Random Walk and Network Measures,2015. 10.1016/j.eswa.2017.09.014

      [21] Sheethal Abraham, Sumy Joseph, ”Rare And Frequent Weighted Itemset Optimization Using Homologous Transactions: A Rule Mining Ap-proach”, J, 2015 International Conference on Control, Communication and Computing India (ICCC) ,November 2015

      [22] Jerry Chun, wensheng Gan, Philippe Fournier, ”High Utility mining and Privacy prteserving utility mining, Elsevier, 2016

      [23] Tamir Tassa, ”Secure Mining of association Rules in Horizontally Distributed Database”, IEEE Transaction, 2013

      [24] Luca Cagliero, Discovering Temporal Change Patterns in the Presence of Taxonomies, IEEE Transaction, 2013

      [25] Sarra Gacem, Djamila Mokeddem , Hafida Belbachir, ”Privacy Preserv-ing In Data Mining: Case of Association Rule”, IJCSI, 2013

      [26] Shen Zhong, ”privacy preserving algorithms for Distributed mining of frequent itemsets”, Elsevier, 2007

      [27] Luigi Troiano, Giacomo Scibelli, Cosimo Birtolo , ”A Fast Algorithm for Mining Rare Itemsets”, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

      [28] A. Nor Antonina, N. A. M. Shazili, B. Y. Kamaruzzaman, M. C.

      Ong, Y. Rosnan, F. N. Sharifah ”Geochemistry of the Rare Earth El

      ments(REE) Distribution in Terengganu Coastal Wa ters: A Study

      Case from Redang Island Marine Sediment ”, 2013

      http://dx.doi.org/10.4236/ojms.2013.33017

      [29] Mehdi Adda1, Lei Wu2, Yi Feng3 , ” Rare Itemset Mining”, Sixth

      International Conference on Machine Learning and Applictions,2007.

      [30] Monika Akbar, Rafal A. Angryk ”Frequent Pattern-Growth Ap

      proach for Document Organization”,ONISW, 2008

      [31] Junfeng Ding, Stephen S.T. Yau ”TCOM, an innovative data struc

      ture for mining association rules among infrequent items”,Elsevier

      2009.

      [32] Laszlo Szathmary, Petko Valtchev ”Towards Rare Itemset Min-

      ing”,19th IEEE International Conference on Tools with Artificial In-

      telligence,2007.

      [33]PaoloGarza,FabioPulvirenti,LucaVenturin ”Frequent Itemsets Minin

      for Big Data: A Comparative Analy

      sis”,https://doi.org/10.1016/j.bdr.2017.06.006

      [34]Ms.KalyaniTukaramBhandwalkar,Ms.MansiBhonsle ”Study of Infre-

      quent itemset mining Techniques”,International Journal of Engineer

      ing Research and General Science Volume 2, Issue 6, October-

      November, 2014.

      [35]Ashish Gupta, Akshay Mittal, Arnab Bhattacharya ”Minimally Infre-

      quent Itemset Mining using Pattern-Growth Paradigm and Residual

      Trees”,17th International Conference on Management of Data ,2011


 

View

Download

Article ID: 15225
 
DOI: 10.14419/ijet.v7i3.8.15225




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.