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Finding ‘‘interesting’’ trends in social networks using frequent pattern

Finding ‘‘interesting’’ trends in social networks using frequent pattern mining and self organizing maps. Presenter : Min-Cong Wu Authors : Puteri N.E. Nohuddin , Frans Coenen , Rob Christley , Christian Setzkorn , Yogesh Patel , Shane Williams c 2012.KBS. Outlines. Motivation

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Finding ‘‘interesting’’ trends in social networks using frequent pattern

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  1. Finding ‘‘interesting’’ trends in social networks using frequent pattern mining and self organizing maps Presenter : Min-Cong WuAuthors : Puteri N.E. Nohuddin, FransCoenen, Rob Christley, Christian Setzkorn, YogeshPatel , Shane Williams c2012.KBS

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • Number of trends may be identified, too many to allow simple inspection by decision makers. Some mechanism was therefore required to allow the simple presentation of trend lines.

  4. Objectives • Generating frequent pattern trends,and use SOM technology a process for assisting the analysis of the identified trends, and to identify ‘‘interesting’’ changes in trends.

  5. Methodology-The trend mining mechanism

  6. Methodology - Frequent pattern trend mining (TM-TFP) Input: Data set :{t1,t2,..,tn}, ti={a,…,z} a={a1,a2,…,an}, support:3 Interestpattern: {a,c,s} Example: support:3 Interestpattern: {a,c,s} ID Item set ordered

  7. Methodology - Frequent pattern trend mining (TM-TFP) id {a,b,c,d}={0,0,2500,3311,2718,0,0,0,2779} {a,b,c,e}={3,12,6,0,100,2437,0,56,79} {a,c,e,f}={0,0,0,2568,345,23,90,0,459} Conditions Target Frequent pattern Conditions Target tree ID m m

  8. Methodology – Trend clustering Input: v1,v1,..,vn Process: ||V – Wi || =  min { ||V – Wj || } Output: BMU

  9. Methodology – Trend clusters analysis e*k

  10. Experiment - Cattle movement database

  11. Experiment - Cattle movement trend mining

  12. Experiment - Deeside Insurance database

  13. Experiment - Deeside Insurance trend mining

  14. Conclusions • By employing the SOM clustering technique, the large number of trend lines that are typically identified may be grouped to facilitate a better understanding of the nature of the trends.

  15. Comments • Advantages - a better understanding of the nature of the trends. Applications - self organizing map

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