CloSpan: Mining Closed Sequential Patterns in Large Datasets

By Xifeng Yan, Jiawei Han, R. Afshar. SDM, 2003

1,058 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/42227c57c221d655b67a95afd2f62477e08340d9

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

1. Jiawei Han
2. Jianyong Wang
3. Yu Zheng
4. Hong Cheng
5. Xifeng Yan
6. Dong Xin
7. Cynthia Rudin
8. Jian Pei

In Information Systems

1. Jiawei Han
2. Yu Zheng
3. Hong Cheng
4. Charles Sutton
5. Xifeng Yan
6. Chanchal Kumar Roy
7. Jian Pei
8. James R. Cordy

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Jiawei Han
4. Hong Cheng
5. Jianyong Wang
6. Chanchal Kumar Roy
7. Xifeng Yan
8. Dong Xin

Citations in the corpus

• Guided GUI testing of android apps with minimal restart and approximate learning (2013, methodology): “The technique combines frequent subsequence mining [115] and inference of finite state machines [19] to infer meaningful action sequences.”
• A Survey of Sequential Pattern Mining (2017, methodology): “To address this issue, a solution that has been extensively studied is to discover concise representations of sequential patterns instead of all sequential patterns [30, 31, 32, 28, 108, 109, 45, 44, 67, 64, 63, 46, 70, 92, 114, 48, 56].”
• Mining Individual Life Pattern Based on Location History (2009, methodology): “LP-Mine applies closet+ and CloSpan techniques respectively for mining (non-temporal) non-sequential life patterns and sequential life patterns from the life sequence dataset.”
• Frequent pattern mining: current status and future directions (2007, methodology): “| | Based on this property, CloSpan can prune the non-closed sequences from further consideration during the mining process.”
• A survey of temporal data mining (2006, methodology): “Although, one can have episodes that are neither serial nor parallel, the episode discovery framework of Mannila et al (1997) is mainly concerned with only these two varieties of episodes.”
• A Survey on Software Clone Detection Research (2007, methodology): “An extended version of CloSpan [221] is used to support gap constraints in frequent subsequences which allows CP-Miner to tolerate one to two statement insertions, deletions, or modifications in copy-pasted code while ignoring arbitrarily long different copy-pasted segment that is unlikely to be cop”
• Mining sequential patterns by pattern-growth: the PrefixSpan approach (2004, methodology): “A recent study in [27] proposed an efficient closed sequential pattern method, called CloSpan, as a further development of the PrefixSpan mining framework, influenced by this approach.”
• Sequential pattern mining -- approaches and algorithms (2013, background): “Closed Sequential pattern mining [Yan et al. 2003] follows the candidate generation and test paradigm and stores the generated sets of candidates in a hash-indexed result­tree following which postpruning is conducted to produce the closed set of frequent sequences.”
• BIDE: efficient mining of frequent closed sequences (2004, methodology): “Because CloSpan needs to maintain the set of historical closed sequence candidates, it will consume much memory and lead to huge search space for pattern closure checking when there are many frequent closed sequences.”
• Swarm (2010, background): “Second, although the problem is defined using the similar form of frequent pattern mining [1, 11], none of previous work [1, 11, 25, 23, 19, 21] solves exactly the same problem as finding swarms.”
• On Reinforcement Learning for Full-length Game of StarCraft (2018, methodology): “• Secondly, we use a prefix-span (19) algorithm to mine the relationship of the each operation and combine the related operations to be a sequence of actions a of which max length isC and constructed a setA which is defined asA = { a = (a0, a1, a2, .”
• Trajectory Data Mining (2015, methodology): “After the transformation, we can mine the sequential patterns from these sequences by using existing sequential pattern mining algorithms such as PrefixSpan [Pei et al. 2011] and CloseSpan [Yan et al. 2003], with time constraints.”
• Effective Pattern Discovery for Text Mining (2012, methodology): “A variety of efficient algorithms such as Apriori-like algorithms [2], [31], [49], PrefixSpan[32],[ 53 ],FP-tree[10],[11],SPADE[56],SLPMiner [42],andGST[12]havebeenproposed.Theseresearchworks have mainly focused on developing efficient mining algorithms for discovering patterns from a large data col”
• CP-Miner: A Tool for Finding Copy-paste and Related Bugs in Operating System Code (2004, methodology): “Due to space limitation, a detailed discussion of the CloSpan algorithm can be found in [29, 38].”
• VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery (2017, methodology): “Due to CloSpan’s heuristics for improved efficiency, CP-Miner can scale to the size of moderately large code bases such as the Linux kernel, by consuming less memory space.”
• CP-Miner: finding copy-paste and related bugs in large-scale software code (2006, methodology): “Frequent subsequence mining is an active research topic in data mining [36].”