PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth

By Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Helen Pinto, Qiming Chen, Umeshwar Dayal, Meichun Hsu. IEEE International Conference on Data Engineering, 2001

748 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3828b947ca05b56be3619a2dd4594ea738dbc228

How researchers see this paper

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

1. Charu C. Aggarwal
2. Fosca Giannotti
3. Bamshad Mobasher
4. Yizhou Sun
5. Dino Pedreschi
6. Yu Zheng
7. Robin D. Burke
8. Mirco Nanni

Citations in the corpus

• p-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data (2016, methodology): “We adapt PrefixSpan [29] as it has proved to be one of the most efficient sequential pattern mining algorithms.”
• Co-author Relationship Prediction in Heterogeneous Bibliographic Networks (2011, methodology): “We further extract frequent phrases from titles as topics using the sequential pattern mining algorithm PrefixSpan [10].”
• Trajectory pattern mining (2007, methodology): “the more recent PrefixSpan [9] and SPADE [12].”
• Context-aware music recommendation based on latenttopic sequential patterns (2012, methodology): “Inspired with the idea of the FP-growth[6] approach, PrefixSpan[11] can be used for sequential pattern mining without generating candidate sequences at each step.”
• A taxonomy of sequential pattern mining algorithms (2010, methodology): “Experimental re-sults show that for disk-based processing, it is better to use bilevel projection, while for main memory processing using pseudo-projection gives a better performance [Pei et al. 2001].”