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  • *Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996] < >Support How many people? 6Frequency What proportion? 6/10A frequent behavior is followed by at least 50% of peopleis a frequent sequence!

    RosaFrdrickFranoisBrunoPascalOsmarSandraMaguelonneJulienAlberto

  • *Pattern Discovery: What if contextual information is available?Contextual InformationWorking or Not working

    RosaFrdrickFranoisBrunoPascalOsmarSandraMaguelonneJulienAlberto

  • *Pattern Discovery: What if contextual information is available?Contextual InformationWorking or Not working

    RosaFrdrickFranoisBrunoPascalOsmarSandraMaguelonneJulienAlberto

  • *Pattern Discovery: What if contextual information is available?Contextual InformationWorking or Not working

    RosaFrdrickFranoisBrunoPascalOsmarSandraMaguelonneJulienAlberto

  • *Pattern Discovery: What if contextual information is available?Contextual InformationWorking or Not working

    RosaFrdrickFranoisBrunoPascalOsmarSandraMaguelonneJulienAlberto

  • *Pattern Discovery: What if contextual information is available?Contextual InformationWorking or Not working

    RosaFrdrickFranoisBrunoPascalOsmarSandraMaguelonneJulienAlberto

  • *Pattern Discovery: What if contextual information is available?Contextual InformationWorking or Not working

    RosaFrdrickFranoisBrunoPascalOsmarSandraMaguelonneJulienAlberto

  • *Pattern Discovery: What if contextual information is available?Contextual InformationWorking or Not working

    RosaFrdrickFranoisBrunoPascalOsmarSandraMaguelonneJulienAlberto

    Sequential data******As opposed to the previous examples, **


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Randomization in Privacy Preserving Data Mining Agrawal, R., and Srikant, R. Privacy-Preserving Data Mining, ACM SIGMOD’00 the following slides include

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Paper Authors: Rakesh Agrawal, Jerry Kiernan, Ramakrishnan Srikant, Yirong Xu Presented By: Camille Gaspard Originally taken from: pages.cpsc.ucalgary.ca/~hammad/Fall04-00_files/Reg_Hippocratic%20Databases2.ppt

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Srikant Rao and Ravindra Kini: Affordable Business Solutions · India. This meant frequent transfers from city to city and a new home every few years. Srikant attended schools in

Practical Privacy: The SuLQ Framework · In 2000 Lindell and Pinkas [11] and Agrawal and Srikant [2] presented two approaches to pri-vacy preserving datamining. Lindell and Pinkas

Compiler Design Prof. Y. N. Srikant Department of Computer ...textofvideo.nptel.ac.in/106108052/lec1.pdf · Compiler Design . Prof. Y. N. Srikant . Department of Computer Science

Mining Sequential Patterns Rakesh Agrawal Ramakrishnan Srikant Proc. of the Int’l Conference on Data Engineering (ICDE) March 1995 Presenter: Phil Schlosser

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Privacy-Preserving Data Mining Rakesh Agrawal Ramakrishnan Srikant IBM Almaden Research Center 650 Harry Road, San Jose, CA 95120 Published in: ACM SIGMOD

Fast Algorithms For Mining Association Rules By Rakesh Agrawal and R. Srikant Presented By: Chirayu Modi

A Survey of Parallel Sequential Pattern Mining · 2019-04-08 · A Survey of Parallel Sequential Pattern Mining • 00:3 and Srikant. It is designed for finding frequent itemsets

Order Preserving Encryption for Numeric Data Rakesh Agrawal Jerry Kiernan Ramakrishnan Srikant Yirong Xu IBM Almaden Research Center

Catalog Integration R. Agrawal, R. Srikant:

Frequent Itemset Mining Methods. The Apriori algorithm Finding frequent itemsets using candidate generation Seminal algorithm proposed by R. Agrawal and