COBRA: Closed Sequential Pattern Mining Using Bi-phase Reduction Approach

Huang, Kuo-Yu; Chang, Chia‐Hui; Tung, Jiun-Hung; Ho, Cheng-Tao

doi:10.1007/11823728_27

Cited by 16 publications

(14 citation statements)

References 11 publications

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“…Huang et al [8] proposed the COBRA algorithm. It extends a frequent sequence with closed itemsets instead of single items.…”

Section: Cobramentioning

confidence: 99%

A survey on closed sequential pattern mining

Raju¹,

Varma²

2014

International Conference on Information Communication and Embedded Systems (ICICES2014)

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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. Closed sequential pattern mining produces more compact result set and it is more efficient than sequential pattern mining. In this paper, we present the some of the popular algorithms in closed sequential pattern mining and its extensions.

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“…Huang et al [8] proposed the COBRA algorithm. It extends a frequent sequence with closed itemsets instead of single items.…”

Section: Cobramentioning

confidence: 99%

A survey on closed sequential pattern mining

Raju¹,

Varma²

2014

International Conference on Information Communication and Embedded Systems (ICICES2014)

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“…COBRA by Huang et al [19] uses several pruning techniques while traversing the prefixtree. The authors show that a closed sequence has only closed itemsets as elements.…”

Section: Sequence Miningmentioning

confidence: 99%

Mining Rank Data

Henzgen

Hüllermeier

2019

ACM Trans. Knowl. Discov. Data

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The problem of frequent pattern mining has been studied quite extensively for various types of data, including sets, sequences, and graphs. Somewhat surprisingly, another important type of data, namely rank data, has received very little attention in data mining so far. In this paper, we therefore addresses the problem of mining rank data, that is, data in the form of rankings (total orders) of an underlying set of items. More specifically, two types of patterns are considered, namely frequent rankings and dependencies between such rankings in the form of association rules. Algorithms for mining frequent rankings and frequent closed rankings are proposed and tested experimentally, using both synthetic and real data. arXiv:1806.05897v1 [cs.LG] 15 Jun 2018Obviously, the notions of support, maximality, and closedness, which have been introduced here for rankings, can be used for orders in exactly the same way.

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“…The COBRA algorithm was developed by Huang et al [14]. It adopts a bi-phase reduction method for closed sequential pattern mining.…”

Section: Related Workmentioning

confidence: 99%

Mining Closed Sequential Patterns in Large Sequence Databases

Raju¹,

Varma²

2015

IJDMS

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Sequential pattern mining is studied widely in the data mining community. Finding sequential patterns is a basic data mining method with broad applications. Closed sequential pattern mining is an important technique among the different types of sequential pattern mining, since it preserves the details of the full pattern set and it is more compact than sequential pattern mining. In this paper, we propose an efficient algorithm CSpan for mining closed sequential patterns. CSpan uses a new pruning method called occurrence checking that allows the early detection of closed sequential patterns during the mining process. Our extensive performance study on various real and synthetic datasets shows that the proposed algorithm CSpan outperforms the CloSpan and a recently proposed algorithm ClaSP by an order of magnitude.

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COBRA: Closed Sequential Pattern Mining Using Bi-phase Reduction Approach

Cited by 16 publications

References 11 publications

A survey on closed sequential pattern mining

A survey on closed sequential pattern mining

Mining Rank Data

Mining Closed Sequential Patterns in Large Sequence Databases

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