Ramakrishnan Srikant

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Ramakrishnan Srikant
Ramakrishnan Srikant.jpg
Ramakrishnan Srikant (2005)
Alma mater University of Wisconsin
Awards ACM Fellow (2014) [1]
Scientific career
Fields Computer Science
Institutions IBM, Google
Thesis Fast Algorithms for Mining Association Rules and Sequential Patterns  (1996)
Doctoral advisor Rakesh Agrawal
Jeffrey Naughton
Website rsrikant.com

Ramakrishnan Srikant is a Google Fellow at Google.

His primary field of research is Data Mining. His 1994 paper, "Fast algorithms for mining association rules", [2] co-authored with Rakesh Agrawal has acquired over 27000 citations as per Google Scholar [3] as of July 2014, and is thus one of the most cited papers in the area of Data Mining. It won the VLDB 10-year award in 2004. [4] His 1995 paper, Mining Sequential Patterns, [5] also co-authored with Rakesh Agrawal, was awarded the ICDE Influential Paper Award in 2008, [6] and his 2004 paper, Order-Preserving Encryption for Numeric Data, [7] co-authored with Rakesh Agrawal, Jerry Kiernan and Yirong Xu, won the 2014 SIGMOD Test of Time Award. [8]

Srikant is a winner of the Grace Murray Hopper Award [9] and was also awarded the SIGKDD Innovation Award in the year 2006. [10]

He was elected to Fellow of ACM (2014) for contributions to knowledge discovery and data mining. [11]

Related Research Articles

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. In any given transaction with a variety of items, association rules are meant to discover the rules that determine how or why certain items are connected.

Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.

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References

  1. Ramakrishnan Srikant ACM Fellows 2014
  2. Fast algorithms for mining association rules
  3. "Google Scholar".
  4. "VLDB 10 years Awards".
  5. Mining Sequential Patterns
  6. "ICDE Influential Paper Awards".
  7. Order-Preserving Encryption for Numeric Data
  8. "2014 SIGMOD Test of Time Award – SIGMOD Website".
  9. "Ramakrishnan Srikant".
  10. "SIGKDD Innovation Award | Sig KDD". www.kdd.org. Archived from the original on 2012-05-26.
  11. Ramakrishnan Srikant ACM Fellows 2014