FreeSpan: frequent pattern-projected sequential pattern mining

By Jiawei Han, Jian Pei, Behzad Mortazavi-Asl, Qiming Chen, Umeshwar Dayal, Meichun Hsu. Knowledge Discovery and Data Mining, 2000

1,014 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c22682774872f66cc74fc4e687d7ea5300f6c19d

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Citations in the corpus

Temporal Data Mining: an overview (2001, background): “Extending this to deal with sequential data is presented in [29].”
Mining sequential patterns by pattern-growth: the PrefixSpan approach (2004, methodology): “Based on this philosophy, we first proposed a straightforward pattern growth method, FreeSpan (for Frequent pattern-projected Sequential pattern mining) [8], which reduces the efforts of candidate subsequence generation.”
Sequential pattern mining -- approaches and algorithms (2013, methodology): “This mining process is outlined below [Han et al. 2000; Pei et al. 2001]: To find the sequential patterns containing only item a.”
Mining frequent patterns by pattern-growth: methodology and implications (2000, background): “... for associations [3; 19], which has sparked the development of various kinds of Apriori-like association mining algorithms, as well as its extensions to mining correlation [8], causality [27], sequential patterns [4], episodes [20], maxpatterns [5], constraint-based mining [29; 21; 17; 12], asso”
A Survey of Sequential Pattern Mining (2017, methodology): “Another notable pattern-growth sequential pattern mining algorithms is FreeSpan [51], an early version of PreﬁxSpan, proposed by the same research team.”
Discovering expressive process models by clustering log traces (2006, background): “, [27], [28], [29], [30], [31]); as an instance, in [30], the problem of classifying sequences is dealt with by considering frequent subsequences of them as relevant features.”
Foundations and Advances in Data Mining (2005, background): “We note that recently, the k-ways normalized cut and spectral clustering received much attention in the machine learning community, and many interesting results about these two approaches have been reported [7, 20, 23, 25, 28, 29, 30].”
A taxonomy of sequential pattern mining algorithms (2010, methodology): “The third step builds level-2-sequences from the candidate sequences in the S-Matrix and ﬁnds annotations for repeating items and projected databases in order to discard the matrix and generate level-3 projected databases; see Table IV, built from parsing the matrix row-wise, bottom-up.”