APRIORI

Complexity analysis of depth first and FP-growth implementations of APRIORI We examine the complexity of Depth First and FP-growth implementations of APRIORI, two of the fastest known data mining algorithms to find frequent itemsets in large databases. We describe the algorithms in a similar style, derive theoretical formulas, and provide experiments on both synthetic and real life data to illustrate the theory.


References in zbMATH (referenced in 4 articles )

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  1. Lin, Ke-Chung; Liao, I-En; Chang, Tsui-Ping; Lin, Shu-Fan: A frequent itemset mining algorithm based on the principle of inclusion-exclusion and transaction mapping (2014) ioport
  2. Rahal, Imad; Ren, Dongmei; Wu, Weihua; Denton, Anne; Besemann, Christopher; Perrizo, William: Exploiting edge semantics in citation graphs using efficient, vertical arm (2006) ioport
  3. Rahal, Imad; Ren, Dongmei; Wu, Weihua; Denton, Anne; Besemann, Christopher; Perrizo, William: Exploiting edge semantics in citation graphs using efficient, vertical ARM (2006) ioport
  4. Kosters, Walter A.; Pijls, Wim; Popova, Viara: Complexity analysis of depth first and FP-growth implementations of APRIORI (2003)