Abstract
Incomplete data in the data mining process is a common case. In collaborative data mining, treating this case can bring better performance and results, so the study of missing data from the perspective of the collaborative data mining approach is addressed in this article. Completing missing data for research sources have been done in two ways: using the average from other sources or adding an operator from the RapidMiner application to the processes. Both approaches have generated good results and can be considered as viable alternatives in collaborative data mining processes.
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
Notes
References
Matei, O., et al.: Multi-layered data mining architecture in the context of Internet of Things. In: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), pp. 1193–1198. IEEE (2017)
Matei, O., et al.: Collaborative data mining for intelligent home appliances. In: Working Conference on Virtual Enterprises, pp. 313–323. Springer, Cham (2016)
Matei, O., et al.: A data mining system for real time soil moisture prediction. Procedia Eng. 181, 837–844 (2017)
Anton, C., Matei, O., Avram, A.: Collaborative Data Mining in Agriculture for Prediction of Soil Moisture and Temperature. Advances in Intelligent Systems and Computing (to appear)
Anton, C., Matei, O., Avram, A.: Use of Multiple Data Sources in Collaborative Data Mining. Computational Methods in Systems and Software (to appear)
Avram, A., et al.: Context-aware data mining vs classical data mining: case study on predicting soil moisture. In: International Workshop on Soft Computing Models in Industrial and Environmental Applications, pp. 199–208. Springer, Cham (2019)
Fayyad, U.M., et al.: Knowledge discovery and data mining: towards a unifying framework. In: KDD, pp. 82–88 (1996)
Grzymala-busse, J.W., Grzymala-busse, W.J.: Handling missing attribute values. In: Data Mining and Knowledge Discovery Handbook, pp. 33–51. Springer, Boston (2009)
Grzymala-busse, J.W., Hu, M.: A comparison of several approaches to missing attribute values in data mining. In: International Conference on Rough Sets and Current Trends in Computing, pp. 378–385. Springer, Heidelberg (2000)
Kotsiantis, S., et al.: Filling missing temperature values in weather data banks. In: 2006 2nd IET International Conference on Intelligent Environments-IE 2006. IET, pp. 327–334 (2006)
Imielinski, T., Lipski Jr., W.: Incomplete information in relational databases. In: Readings in Artificial Intelligence and Databases, pp. 342–360. Morgan Kaufmann (1989)
Imielinski, T., Lipski Jr., W.: Incomplete information and dependencies in relational databases. In: ACM SIGMOD Record, pp. 178–184. ACM (1983)
Radhika, Y., Shashi, M.: Atmospheric temperature prediction using support vector machines. Int. J. Comput. Theory Eng. 1(1), 55 (2009)
Zhang, S., Zhang, C., Yang, Q.: Data preparation for data mining. Appl. Artif. Intell. 17(5–6), 375–381 (2003)
Matei, O., et al.: Context-aware data mining: embedding external data sources in a machine learning process. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 415–426. Springer, Cham (2017)
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
Anton, C.A., Matei, O., Avram, A. (2019). Missing Data in Collaborative Data Mining. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational Statistics and Mathematical Modeling Methods in Intelligent Systems. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-31362-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-31362-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31361-6
Online ISBN: 978-3-030-31362-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)