A Clustering Based Approximate Algorithm for Mining Frequent Itemsets-Reference-Cited by-同舟云学术

A Clustering Based Approximate Algorithm for Mining Frequent Itemsets

Published:2020 Issue: Volume: Page:226-237
ISSN:2367-4512
Container-title:Data Science: From Research to Application
language:
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Author:

Fatemi Seyed Mohsen^ORCID,Hosseini Seyed Mohsen^ORCID,Kamandi Ali^ORCID,Shabankhah Mahmood^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-37309-2_18

Reference17 articles.

1. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993). https://doi.org/10.1145/170036.170072

2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994). http://dl.acm.org/citation.cfm?id=645920.672836

3. Bayardo Jr, R.J.: Efficiently mining long patterns from databases. In: ACM SIGMOD Record, vol. 27, pp. 85–93. ACM (1998)

4. Deng, Z., Wang, Z.: A new fast vertical method for mining frequent patterns. Int. J. Comput. Intell. Syst. 3, 733–744 (2010). https://doi.org/10.2991/ijcis.2010.3.6.4

5. Fournier-Viger, P., Lin, J.C.W., Vo, B., Chi, T.T., Zhang, J., Le, H.B.: A survey of itemset mining. Wiley Interdisc. Rev.: Data Min. Knowl. Discovery 7(4), e1207 (2017)

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