Abstract
We present an approximate algorithm for finding frequent itemsets. The main idea can be described as turning the problem of mining frequent itemsets into a clustering problem. More precisely, we first represent each transaction by a vector using one-hot encoding scheme. Then, by means of mini batch k-means, we group all transactions into a number of clusters. The center of each cluster can be assumed as a potential candidate for a frequent itemset. To test the validity of this assumption, we compute the support of itemsets represented by cluster centers. All clusters that do not meet the minimum support condition will be removed from the set of clusters. As our experiments show, this approximate algorithm can capture more than 90% of all frequent itemsets at a much faster rate than the competing algorithms. Moreover, we show that the execution time of our algorithm is linear.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
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
Bayardo Jr, R.J.: Efficiently mining long patterns from databases. In: ACM SIGMOD Record, vol. 27, pp. 85–93. ACM (1998)
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
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)
Hahsler, M., Grün, B., Hornik, K., Buchta, C.: Introduction to arules – a computational environment for mining association rules and frequent item sets (2005)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2), 1–12 (2000). https://doi.org/10.1145/335191.335372
Kaur, J., Madan, N.: Association rule mining: a survey. Int. J. Hybrid Inf. Technol. 8(7), 239–242 (2015)
McIntosh, T., Chawla, S.: High confidence rule mining for microarray analysis. IEEE/ACM Trans. Comput. Biol. Bioinf. (TCBB) 4(4), 611–623 (2007)
Park, J.S., Chen, M.S., Yu, P.S.: An effective hash-based algorithm for mining association rules. SIGMOD Rec. 24(2), 175–186 (1995). https://doi.org/10.1145/568271.223813
Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: hyper-structure mining of frequent patterns in large databases. In: Proceedings 2001 IEEE International Conference on Data Mining, pp. 441–448, November 2001. https://doi.org/10.1109/ICDM.2001.989550
Savasere, A., Omiecinski, E., Navathe, S.B.: An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21th International Conference on Very Large Data Bases, VLDB 1995, pp. 432–444. Morgan Kaufmann Publishers Inc., San Francisco (1995). http://dl.acm.org/citation.cfm?id=645921.673300
Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 1177–1178. ACM, New York (2010). https://doi.org/10.1145/1772690.1772862
Uno, T., Kiyomi, M., Arimura, H.: Efficient mining algorithms for frequent/closed/maximal itemsets. In: Proceedings of the IEEE ICDM Workshop Frequent Itemset Mining Implementations (2004)
Vo, B., Hong, T.P., Le, B.: Dynamic bit vectors: an efficient approach for mining frequent itemsets. Sci. Res. Essays 6(25), 5358–5368 (2011)
Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000). https://doi.org/10.1109/69.846291
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fatemi, S.M., Hosseini, S.M., Kamandi, A., Shabankhah, M. (2020). A Clustering Based Approximate Algorithm for Mining Frequent Itemsets. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-030-37309-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-37309-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37308-5
Online ISBN: 978-3-030-37309-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)