BIDE: efficient mining of frequent closed sequences

By Jianyong Wang, Jiawei Han. Proceedings / International Conference on Data Engineering, 2004

746 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/d297219f6add05d281d39011b95977d3e30ddaac

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. Jiawei Han
2. Jaroslav M. Fowkes
3. Hong Cheng
4. Charles Sutton
5. Jian Pei
6. Xifeng Yan
7. Dong Xin
8. Cynthia Rudin

In Information Systems

1. Jaroslav M. Fowkes
2. Charles Sutton
3. Hong Cheng
4. Jiawei Han
5. Xifeng Yan
6. Jian Pei
7. Dong Xin
8. Cynthia Rudin

Citations in the corpus

• Parameter-Free Probabilistic API Mining at GitHub Scale (2015, methodology): “The BIDE algorithm [35] uses bidirectional search to mine frequent closed sequences (i.”
• 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].”
• A taxonomy of sequential pattern mining algorithms (2010, methodology): “With the increase in the use of the world wide web for e-commerce businesses, web services, and others, web usage mining is one of the most prevalent application areas of sequential pattern mining in the literature [Pei et al. 2000; Lu and Ezeife 2003; El-Sayed et al. 2004; Wang and Han 2004; Goetha”
• Frequent pattern mining: current status and future directions (2007, methodology): “A later algorithm called BIDE , a bidirectional search for mining frequent closed sequences was developed by Wang and Han (2004), which can further optimize this process by projecting sequence datasets in two directions.”
• MAPO: mining API usages from open source repositories (2006, methodology): “Acknowledgments We would like to thank Jiawei Han and Jianyong Wang for helping us to use the BIDE tool.”
• Mining API patterns as partial orders from source code: from usage scenarios to specifications (2007, methodology): “Frequent subsequences [22, 26] characterize sequences of API calls that are often used together while considering their orders.”
• Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns (2015, background): “As with all traditional pattern mining approaches, redundancy is also a key problem when mining sequential patterns [33, 9].”
• A survey of temporal data mining (2006, methodology): “The algorithm by Wang & Han (2004) is particularly interesting in that it presents an efficient method for mining sequential closed patterns without an explicit iterative candidate generation step.”
• Parameter-free probabilistic API mining across GitHub (2015, methodology): “One way to address this is by mining frequent closed sequences, i.e., those that have no subsequences with the same frequency, such as via the BIDE algorithm [39].”