Mining sequential patterns by pattern-growth: the PrefixSpan approach

By Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Jianyong Wang, Helen Pinto, Qiming Chen, Umeshwar Dayal, Meichun Hsu. IEEE Transactions on Knowledge and Data Engineering, 2004

1,521 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/785e5d2081d43d7659c5402c0d40552a332d6adc

How researchers see this paper

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

In Artificial Intelligence

1. Philip S. Yu
2. B. Liu
3. Jiawei Han
4. Hong Cheng
5. Charu C. Aggarwal
6. Bing Liu
7. Xifeng Yan
8. Dong Xin

In Computational Theory and Mathematics

1. Hong Cheng
2. Xifeng Yan
3. Dong Xin
4. Philippe Fournier-Viger
5. Jilles Vreeken
6. Jiawei Han
7. Philip S. Yu
8. Roel Bertens

In Information Systems

1. Philip S. Yu
2. Hong Cheng
3. B. Liu
4. Jiawei Han
5. Xifeng Yan
6. Dong Xin
7. Philippe Fournier-Viger
8. Yun Sing Koh

Citations in the corpus

• Discriminative Subsequence Mining for Action Classification (2007, methodology): “[14], which is an algorithm to enumerate all frequent subsequences: Find all s ∈ S such that ∑̀”
• Identifying comparative sentences in text documents (2006, background): “Class sequential rules are different from traditional sequential patterns [1, 2, 25] because a class label is attached, which results in a rule with a sequential pattern on the left-hand-side of the rule, and a class on the right-hand-side of the rule.”
• Mining interesting knowledge from weblogs: a survey (2005, methodology): “The comparison includes (i) PSP+, an evolution of GSP, based on candidate generation and test heuristics, (ii) FreeSpan [45], based on the integration of frequent sequence mining and frequent pattern mining, and the newly proposed (iii) PrefixSpan [ 46 ] that uses an approach based on data projectio”
• Frequent pattern mining: current status and future directions (2007, methodology): “A performance comparison of GSP, SPADE, and PrefixSpan shows that PrefixSpan has the best overall performance (Pei et al. 2004).”
• A Survey of Sequential Pattern Mining (2017, methodology): “Depth-first search algorithms such as Spade [117], PrefixSpan [89], Spam [3], Lapin [111], CM-Spam [30],”
• Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns (2015, background): “For sequential data, the key types of patterns studied are frequent subse-quences [21], and frequent episodes [1, 16].”
• Sentiment Classification using Automatically Extracted Subgraph Features (2010, background): “(2005) propose an approach that uses frequent sub-sequence and sub-tree mining approaches (Asai et al., 2002; Pei et al., 2004) to derive structured features such as word sub-sequences and dependency sub-trees.”
• Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases (2013, methodology): “The framework of the proposed methods consists of three steps: 1) Scan the database twice to construct a global UPTree with the first two strategies (given in Section 3.1); 2) recursively generate PHUIs from global UP-Tree and local UP-Trees by UP-Growth with the third and fourth strategies (given…”
• EventAction (2019, background): “We discuss these ethical issues and propose possible solutions.”