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Mining Frequent Item sets in Data Streams [PDF]

Dass, Rajanish
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Parallel mining of closed sequential patterns

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005
Discovery of sequential patterns is an essential data mining task with broad applications. Among several variations of sequential patterns, closed sequential pattern is the most useful one since it retains all the information of the complete pattern set but is often much more compact than it.
Shengnan Cong, Jiawei Han 0001, David A. Padua   +2 more
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IMCS: Incremental Mining of Closed Sequential Patterns

Data & Knowledge Engineering, 2007
Recently, mining compact frequent patterns (for example closed patterns and compressed patterns) has received much attention from data mining researchers. These studies try to address the interpretability and efficiency problems encountered by traditional frequent pattern mining methods.
Lei Chang, Dongqing Yang, Tengjiao Wang 0003, Shiwei Tang   +3 more
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RCA-Seq: An original approach for enhancing the analysis of sequential data based on hierarchies of multilevel closed partially-ordered patterns

Discrete Applied Mathematics, 2020
International audienceMethods for analysing sequential data generally produce a huge number of sequential patterns that have then to be evaluated and interpreted by domain experts.
Cristina Nica, Agnes Braud, Florence Le Ber   +2 more
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Mining Closed Sequential Patterns in Progressive Databases

Journal of Information & Knowledge Management, 2013
Previous studies of Mining Closed Sequential Patterns suggested several heuristics and proposed some computationally effective techniques. Like, Bidirectional Extension with closure checking schemas, Back scan search space pruning, and scan skip optimization used in BIDE (BI-Directional Extension) algorithm.
R. B. V. Subramanyam, A. Suresh Rao, Ramesh Karnati, Soma Raju Suvvari, Durvasula V. L. N. Somayajulu   +4 more
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Mining closed discriminative dyadic sequential patterns

Proceedings of the 14th International Conference on Extending Database Technology, 2011
A lot of data are in sequential formats. In this study, we are interested in sequential data that goes in pairs. There are many interesting datasets in this format coming from various domains including parallel textual corpora, duplicate bug reports, and other pairs of related sequences of events.
David Lo 0001, Hong Cheng 0001, Lucia
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TSP: mining top-K closed sequential patterns

Third IEEE International Conference on Data Mining, 2004
Sequential pattern mining has been studied extensively in the data mining community. Most previous studies require the specification of a min_support threshold for mining a complete set of sequential patterns satisfying the threshold. However, in practice, it is difficult for users to provide an appropriate min_support threshold.
Petre Tzvetkov, Xifeng Yan, Jiawei Han 0001   +2 more
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A survey on closed sequential pattern mining

International Conference on Information Communication and Embedded Systems (ICICES2014), 2014
Sequential pattern mining is an important task in data mining. Sequential pattern mining algorithms developed so far provide better performance for short sequences but these algorithms are inefficient at mining long sequences. To mine long sequences efficiently closed sequential pattern mining was proposed.
V. Purushothama Raju, G. P. Saradhi Varma   +1 more
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Mining Cross-Level Closed Sequential Patterns

2018
Multilevel, cross-level and sequential knowledge plays a significant role in our several real-life aspects including market basket analysis, bioinformatics, texts mining etc. Many researchers have proposed various approaches for mining hierarchical patterns.
Rutba Aman, Chowdhury Farhan Ahmed
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Margin-closed frequent sequential pattern mining

Proceedings of the ACM SIGKDD Workshop on Useful Patterns, 2010
We present a new approach to mining sequential patterns that significantly reduces the number of patterns reported, favoring longer patterns and suppressing shorter patterns with similar frequencies. This is achieved by mining only margin-closed patterns whose support differs by more than some margin from any extension.
Dmitriy Fradkin, Fabian Moerchen
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