Sequential PAttern mining using a bitmap representation

By Jay Ayres, Jason Flannick, Johannes Gehrke, Tomi Yiu. Knowledge Discovery and Data Mining, 2002

1,276 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/ba8dc7a05eccbd32b282a1e367503cf13fea8641

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In Computational Theory and Mathematics

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In Signal Processing

Citations in the corpus

Learning theory analysis for association rules and sequential event prediction (2013, methodology): “This type of work generally considers the order in which items are added, and often uses a Markov assumption (see, for instance, Ayres et al., 2002; Berchtold and Raftery, 2002), whereas in our work, subsets of items are used to predict the next item, possibly without regard to the order in which…”
Sequential pattern mining -- approaches and algorithms (2013, methodology): “SPAM (Sequential PAttern Mining using A Bitmap Representation) [Ayres et al. 2002] uses a novel depth-first traversal of the search space with various pruning mechanisms and a vertical bitmap representation of the database, which enables efficient support counting.”
A Survey of Sequential Pattern Mining (2017, methodology): “Another algorithm that is similar to BitSpade and also performs a depth-ﬁrst search using bit vector IDLists is Spam [3].”
2015 Ieee International Conference on Big Data (big Data) Task-based Recommendation on a Web-scale (neutral): “The research of FPM investigates algorithms for a wide scope of data mining problems, including frequent item set mining [22] , association rule mining [23], frequent sequence mining [24], [25], [26], and frequent tree or graph mining [27], [28].”
MAPO: Mining and Recommending API Usage Patterns (2009, methodology): “Pattern index of " appendToGroup " For each cluster, MAPO adopts a frequent subsequence miner [7] to mine usage patterns from the code snippets in the cluster (Sections 4.2).”
Parameter-free probabilistic API mining across GitHub (2015, positive): “The problem was ﬁrst introduced by Agrawal and Srikant [5] in the context of market basket analysis, which led to a number of other algorithms for frequent sequence mining, including GSP [35], PreﬁxSpan [29], SPADE [43] and SPAM [8].”
USpan: an efficient algorithm for mining high utility sequential patterns (2012, methodology): “For utility-based sequences, we adapt the concept of the Lexicographic Sequence Tree in [5] to the characteristics of q-sequences, and come up with the Lexicographic Qsequence Tree (LQS-Tree) to construct and organize utilitybased q-sequences.”
EventAction (2019, methodology): “For example, PrefixSpan [55] and SPADE [86] reduce the number of data scans, and SPAM [3] uses a bitmap representation to encode the event sequences and accelerates the mining computations with bitwise operations.”
Parameter-Free Probabilistic API Mining at GitHub Scale (2015, background): “SPAM [8] is tailored towards efficiently mining sequences that are very long and adopts a vertical bitmap representation of the database.”
A taxonomy of sequential pattern mining algorithms (2010, methodology): “The generate-and-test feature also appears in SPAM [Ayres et al. 2002], which is considered one of the efﬁcient algorithms for mining sequences.”
Identifying comparative sentences in text documents (2006, background): “Existing sequential pattern mining techniques in data mining use only a single minimum support [1] to control the pattern generation process so that not too many patterns are produced.”