Abstract
A model presented in current paper designed for dynamic classifying of real time cases received in a stream of big sensing data. The model comprises multiple remote autonomous sensing systems; each generates a classification scheme comprising a plurality of parameters. The classification engine of each sensing system is based on small data buffers, which include a limited set of “representative” cases for each class (case-buffers). Upon receiving a new case, the sensing system determines whether it may be classified into an existing class or it should evoke a change in the classification scheme. Based on a threshold of segmentation error parameter, one or more case-buffers are dynamically regrouped into a new composition of buffers, according to a criterion of segmentation quality.
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
Similar content being viewed by others
References
Fayyad, U., Stolorz, P.: Data mining and KDD: promise and challenges. Future Gener. Comput. Syst. 13, 99–115 (1997). https://doi.org/10.1016/S0167-739X(97)00015-0
Gelbard, R., Goldman, O., Spieger, I.: Investigating diversity of clustering methods: an empirical comparison. Data Knowl. Eng. 63, 155–166 (2007)
Fan, J., Han, F., Han, L.: Challenges of big data analysis. Natl. Sci. Rev., 293–314 (2014)
Darema, F.: Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 662–669. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24688-6_86
Ren, K., Wang, C., Wang, Q.: Security challenges for the public cloud. IEEE Internet Comput. 16, 69–73 (2012). https://doi.org/10.1109/MIC.2012.14
Li, J., Xu, H.: Suggest what to tag: recommending more precise hashtags based on users’ dynamic interests and streaming tweet content. Knowl.-Based Syst. 106, 196–205 (2016). https://doi.org/10.1016/j.knosys.2016.05.047
Miller, Z., Dickinson, B., Deitrick, W., et al.: Twitter spammer detection using data stream clustering. Inf. Sci. 260, 64–73 (2014). https://doi.org/10.1016/j.ins.2013.11.016
Siddharth, S., Chauhan, N.C., Bhandery, S.D.: Incremental mining of association rules: a survey. Int. J. Comput. Sci. Inf. Technol. 3, 4041–4074 (2012)
Cheung, D.W., Han, J., Ng, V.T., Wong, C.: Maintenance of discovered association rules in large databases: An Incremental Updating Technique, pp. 106–114 (1996)
Grira, N., Crucianu, M., Boujemaa, N.: Unsupervised and semi-supervised clustering: a brief survey. In: Review of Machine Learning Techniques for Processing Multimedia Content (2005)
Sreedhar, G.: Web Data Mining and the Development of Knowledge-Based Decision Support Systems. IGI Global, Hershey (2016)
Song, Y.C., Meng, H.D., Wang, S.L., et al.: Dynamic and incremental clustering based on density reachable. In: Fifth International Joint Conference on INC, IMS and IDC, 2009. NCM 2009, pp. 1307–1310 (2009)
Mishra, N., Hsu, M., Dayal, U.: Computer implemented scalable, incremental and parallel clustering based on divide and conquer (2002)
Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44342-2
Jain, A.K., Murty, M.N., Flynn, P.L.: Data clustering: a survey. ACM Comput. Surv. 31, 264–323 (1999)
Vempala, S., Wang, G., Kannan, R., Cheng, D.: Techniques for clustering a set of objects (2010)
Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An efficient algorithm for the incremental updation of association rules in large databases (1997)
Zhang, C-Q., Ou, Y.: Method for data clustering and classification by a graph theory model - network partition into high density subgraphs (2009)
Lughofer, E.: A dynamic split-and-merge approach for evolving cluster models. Evol. Syst. 3, 135–151 (2012). https://doi.org/10.1007/s12530-012-9046-5
Toth, C.K., Grejner-Brzezinska, D.: Extracting dynamic spatial data from airborne imaging sensors to support traffic flow estimation. ISPRS J. Photogramm. Remote Sens. 61, 137–148 (2006). https://doi.org/10.1016/j.isprsjprs.2006.09.010
Zhang, Y., He, S., Chen, J.: Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks. IEEE/ACM Trans. Netw. 24, 1632–1646 (2016). https://doi.org/10.1109/TNET.2015.2425146
Okeyo, G., Chen, L., Wang, H., Sterritt, R.: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive Mob. Comput. 10, 155–172 (2014). https://doi.org/10.1016/j.pmcj.2012.11.004
Ni, Q., Patterson, T., Cleland, I., Nugent, C.: Dynamic detection of window starting positions and its implementation within an activity recognition framework. J. Biomed. Inform. 62, 171–180 (2016). https://doi.org/10.1016/j.jbi.2016.07.005
Download Python. In: Python.org. https://www.python.org/downloads/. Accessed 20 Apr 2018
Ben-David, A.: Automatic generation of symbolic multiattribute ordinal knowledge - based DSS’s: methodology and applications. Decis. Sci. 23, 1357–1372 (1992)
UCI Machine Learning Repository: Data Sets. https://archive.ics.uci.edu/ml/datasets.html. Accessed 10 Dec 2016
Bohanec, M., Rajkovic, V.: Knowledge acquisition and explanation for multi-attribute decision making, pp. 1–19 (1988)
Bittmann, R.M., Gelbard, R.: Visualization of multi-algorithm clustering for better economic decisions—the case of car pricing. Decis. Support Syst. 47, 42–50 (2009). https://doi.org/10.1016/j.dss.2008.12.012
Acknowledgment
This work was supported in part by a grant from the MAGNET program of the Israeli Innovation Authority; who also submitted this work as a patent application.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Gelbard, R., Khalemsky, A. (2018). Dynamic Classifier and Sensor Using Small Memory Buffers. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2018. Lecture Notes in Computer Science(), vol 10933. Springer, Cham. https://doi.org/10.1007/978-3-319-95786-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-95786-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95785-2
Online ISBN: 978-3-319-95786-9
eBook Packages: Computer ScienceComputer Science (R0)