Abstract
Currently incremental mining techniques can be divided into two groups: direct-update technique and batch-update technique. Mining closed item sets is one of the core tasks of data mining. In addition, advances in hardware technology and information technology have created huge data streams in recent years. Therefore, mining incrementally closed item sets over data streams with the batch-update technique is necessary. Incremental algorithms are always associated with an intermediate structure such as tree, lattice, table… In the previous study, the author proposed an intermediate structure which is a linear list called constructive set. In this paper, an incremental mining algorithm based on the constructive with the batch-update technique is proposed in order to mine data streams.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ceglar, A., Roddick, J.F.: Incremental association mining using a closed-set lattice. J. Res. Pract. Inf. Technol. 39(1), 35–45 (2007)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)
Giannella, C., Han, J., Pei, J., Yan, X., Yu, P.S.: Mining frequent patterns in data streams at multiple time granularities. In: Kargupta, H., Joshi, A., Sivakumar, K., Yesha, Y. (eds.) Data Mining: Next Generation Challenges and Future Directions. AAAI/MIT (2003)
Gupta, A., Bhatnagar, V., Kumar, N.: Mining closed itemsets in data stream using formal concept analysis. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2010. LNCS, vol. 6263, pp. 285–296. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15105-7_23
Hu, K., Lu, Y., Shi, C.: Incremental discovering association rules: a concept lattice approach. In: Proceeding of PAKDD99, Beijing, pp. 109–113 (1999)
Jin, R., Agrawal, G.: An algorithm for in-core frequent itemset mining on streaming data. In: ICDM 2005 Proceedings of the Fifth IEEE International Conference on Data Mining, pp. 210–217, 27–30 November 2005
La, P.-T., Le, B., Vo, B.: Incrementally building frequent closed itemset lattice. Expert Syst. Appl. 41(6), 2703–2712 (2014)
Nguyen, T.-T.: Mining incrementally closed item sets with constructive pattern set. Expert Syst. Appl. 100, 41–67 (2018)
Rouane-Hacene, M., Huchard, M., Napoli, A., Valtchev, P.: Relational concept analysis: mining concept lattices from multi-relational data. Ann. Math. Artif. Intell. 67(1), 81–108 (2013)
Szathmary, L., Valtchev, P., Napoli, A., Godin, R., Boc, A., Makarenkov, V.: Fast mining of iceberg lattices: a modular approach using generators. In: CLA, volume 959 of CEUR Workshop Proceedings, pp. 191–206 (2011)
Szathmary, L., Valtchev, P., Napoli, A., Godin, R., Boc, A., Makarenkov, V.: A fast compound algorithm for mining generators, closed itemsets, and computing links between equivalence classes. Ann. Math. Artif. Intell. 70, 81–105 (2013)
Valtchev, P., Missaoui, R.: Building concept (galois) lattices from parts: generalizing the incremental methods. In: Delugach, H.S., Stumme, G. (eds.) ICCS-ConceptStruct 2001. LNCS (LNAI), vol. 2120, pp. 290–303. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44583-8_21
Valtchev, P., Missaoui, R., Godin, R.: A framework for incremental generation of closed itemsets. Discrete Appl. Math. 156(6), 924–949 (2008)
Vo, B., Hong, T.P., Le, B.: A lattice-based approach for mining most generalization association rules. Knowl.-Based Syst. 45, 20–30 (2013)
Totad, S.G., Geeta, R.B., Prasad Reddy, P.V.G.D.: Batch processing for incremental FP-tree construction. Int. J. Comput. Appl. IJCA 5(5), 28–32 (2010)
Totad, S.G., Geeta, R.B., Prasad Reddy, P.V.G.D.: Batch incremental processing for FP-tree construction using FP-Growth algorithm. Knowl. Inf. Syst. 33, 475–490 (2012)
Acknowledgement
This work is funded by Hong Bang International University under grant code GV1907.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, TT., Nguyen, Q., Hung, N.T. (2019). Mining Incrementally Closed Itemsets over Data Stream with the Technique of Batch-Update. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-35653-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35652-1
Online ISBN: 978-3-030-35653-8
eBook Packages: Computer ScienceComputer Science (R0)