Maximally informative k-itemset mining from massively distributed data streams
Pages 502 - 509
Abstract
We address the problem of mining maximally informative k-itemsets (miki) in data streams based on joint entropy. We propose PentroS, a highly scalable parallel miki mining algorithm. PentroS renders the mining process of large volumes of incoming data very efficient. It is designed to take into account the continuous aspect of data streams, particularly by reducing the computations of need for updating the miki results after arrival/departure of transactions to/from the sliding window. PentroS has been extensively evaluated using massive real-world data streams. Our experimental results confirm the effectiveness of our proposal which allows excellent throughput with high itemset length.
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Index Terms
- Maximally informative k-itemset mining from massively distributed data streams
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Published In
April 2018
2327 pages
ISBN:9781450351911
DOI:10.1145/3167132
- Conference Chairs:
- Hisham M. Haddad,
- Roger L. Wainwright,
- Richard Chbeir
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Published: 09 April 2018
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