SPADE: An Efficient Algorithm for Mining Frequent Sequences

By Mohammed J. Zaki. Machine-mediated learning, 2004

2,326 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/6fadd3cfc15b865885a12bba3f8a707d00dcb25f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Information Systems

In Signal Processing

Citations in the corpus

Mining 3D Key-Pose-Motifs for Action Recognition (2016, background): “There a lot of work [1, 20, 32] when the sequence database is certain (or deterministic) rather than probabilistic.”
A Survey of Sequential Pattern Mining (2017, methodology): “Recently, the Spam [3] and BitSpade [117] algorithms were improved to obtain the CM-Spam and CMSpade algorithms [30].”
Efficiently mining frequent trees in a forest (2002, methodology): “T REEM INERV is in the same spirit as the previous vertical mining algorithms we developed for enumerating frequent itemsets (Eclat [12]) and sequences (SPADE [13]).”
A taxonomy of sequential pattern mining algorithms (2010, neutral): “[Zaki 2001] – Apriori-Based and Pattern-Growth Hybrid Miner.”
Trajectory pattern mining (2007, methodology): “the more recent PrefixSpan [9] and SPADE [12].”
Frequent pattern mining: current status and future directions (2007, methodology): “Zaki (2001) developed a vertical format-based sequential pattern mining method called SPADE , which is an extension of vertical format-based frequent itemset mining methods, like Eclat and CHARM (Zaki 1998; Zaki and Hsiao 2002).”
Association mining (2006, methodology): “In the pursuit of optimizing long itemset discovery, the same authors developed Depth Project [Agrawal et al. 2000].”
CP-Miner: A Tool for Finding Copy-paste and Related Bugs in Operating System Code (2004, background): “Frequent subsequence mining is an active research topic in data mining [38, 39].”
Parameter-free probabilistic API mining across GitHub (2015, methodology): “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].”
Discriminative Frequent Pattern Analysis for Effective Classification (2007, background): “Frequent pattern mining has been a focused theme in data mining research with a large number of scalable methods proposed for mining various kinds of patterns including itemsets [2, 10, 27], sequences [3, 16, 26] and graphs [11, 22].”
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.”
Effective Pattern Discovery for Text Mining (2012, background): “Over the years, people have often held the hypothesis that phrase-based approaches could perform better than the termbased ones, as phrases may carry more “semantics” like information.”
Sequential PAttern mining using a bitmap representation (2002, background): “SPADE [12] and PrefixSpan [9] are two of the fastest algorithms that mine sequential patterns.”
BIDE: efficient mining of frequent closed sequences (2004, background): “Among those, SPADE [24], PrefixSpan [17], and SPAM [4] are quite interesting ones.”
From Local Patterns to Global Models: The LeGo Approach to Data Mining (2008, background): “The discovery of frequent patterns has been generalized into more elaborate structures such as sequences, trees and graphs, for example with the aim of discovering frequent fragments in molecules ( Zaki, 2001; Chi et al., 2005; Sevon et al., 2006; Nijssen & Kok, 2004).”
Mining, indexing, and querying historical spatiotemporal data (2004, neutral): “Then, an algorithm that discovers frequent subsequences in a long sequence (e.g., [16]) is applied.”