Abstract
Data field Clustering is a method to group datasets by virtue of the theory data field which sees every data object as a point with evaluated mass, gets the core data objects, iteratively merges them via simulating the mutual interactions and opposite movements hierarchically. However, there exist some bottlenecks and problems where it may restrict the use and application extending to real areas widely. The determination of impact factor-sigma, the evaluation mass process for every object, the selection of the core objects according to their masses, the ratio of sample initially, time resolution as well as the process of the merging core objects are all crucial to the effectiveness and efficiency of the algorithm results. Through analyzing the main process of data field clustering as well as doing experiment with 2 dimensions data sets, a number of problems are found and several feasible measures to improve the data field clustering is put forward. Using test data sets as example, it is preliminary proven that the improved algorithm obtains a favorable result. Furthermore, the improved method contributes to the further application of data field cluster in Intrusion Detection Systems.
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
George, K., Han, E.H., Chameleon, V.K.: A hierarchical clustering algorithm using dynamic modeling. IEEE Comput. 27(3), 329–341 (1999)
Li, X., Zhang, S.C., Wang, S.L.: Advances in data mining applications. Int. J. Data Warehouse. Min. 2(3), i–iii (2006)
Song, M., Hu, X.H., Yoo, I., Koppel, E.: A dynamic and semantically-aware technique for document clustering in biomedical literature. Int. J. Data Warehouse. Min. 5(4), 44–57 (2009). https://doi.org/10.4018/jdwm.2009080703
Silla Jr., C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Disc. 22, 31–72 (2011). https://doi.org/10.1007/s10618-010-0175-9
Gan, W., Li, D.: Dynamic clustering based on data fields. In: Proceedings of the Eleventh International Fuzzy Systems Association World Congress, vol. III (2005)
Yang, K., Tan, T., Zhang, W.: An evidence combination method based on DBSCAN clustering. CMC Comput. Mater. Continua 57(2), 269–281 (2018)
He, L., et al.: A method of identifying thunderstorm clouds in satellite cloud image based on clustering. CMC Comput. Mater. Continua 57(3), 549–570 (2018)
Wang, S.L., Gan, W.Y., Li, D.Y., Li, D.R.: Data field for hierarchical clustering. Int. J. Data Warehouse. Min. 7(2), 43–63 (2011)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Networks 16(3), 645–678 (2005). https://doi.org/10.1109/TNN.2005.845141
Wang, S., Chen, Y.: HASTA: a hierarchical-grid clustering algorithm with data field. Int. J. Data Warehouse. Min. 10(2), 39–54 (2014)
Yuan, H., Wang, S., Li, Y., Fan, J.: Feature selection with data field. Chin. J. Electron. 23(4), 661–665 (2014)
Wang, S., Wang, D., Li, C., Li, Y., Ding, G.: Clustering by fast search and find of density peaks with data field. Chin. J. Electron. 25(3) (2016)
Liao, H.-J., et al.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16–24 (2013)
Han, S., et al.: Research on application of date site in information security. Comput. Digit. Eng.
Acknowledgments
Thanks to vice Professor ZhangHong for proof-reading and great help of refining this paper. He provides biggest support with great patience all the time.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, H., Wei, H., Shen, Y., Wang, H., Pu, Q. (2019). Bottlenecks and Feasible Solutions of Data Field Clustering in Impact Factor, Time Resolution, Selecting Core Objects and Merging Process. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-24274-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24273-2
Online ISBN: 978-3-030-24274-9
eBook Packages: Computer ScienceComputer Science (R0)