Amir Zakaria Marketing Branding Agency | Data Mining, data modeling, interesting trends, Internet of Things, controlled remotely, intelligent tools
Data mining is the process of knowledge discovery in datasets (Roiger, 2017). It summarizes all analysis procedures required in order to identify interesting trends and patterns within data and includes data preparation and data modeling (Gola, 2018).
Data Mining, data modeling, interesting trends, Internet of Things, controlled remotely, intelligent tools, amir zakaria, nazli monajemzadeh, اميرذكريا, نازلي منجم زاده
16079
post-template-default,single,single-post,postid-16079,single-format-standard,ajax_fade,page_not_loaded,,qode-child-theme-ver-1.0.0,qode-theme-ver-10.0,wpb-js-composer js-comp-ver-4.12,vc_responsive

Data Mining

Data Mining

Data mining is the process of knowledge discovery in datasets (Roiger, 2017). It summarizes all analysis procedures required in order to identify interesting trends and patterns within data and includes data preparation and data modeling (Gola, 2018).

In recent years, the Internet of Things (IoT) has emerged as a new opportunity. Thus, all devices such as smartphones, transportation facilities, public services, and home appliances are used as data creator devices. All the electronic devices around us help our daily life. Devices such as wrist watches, emergency alarms, and garage doors and home appliances such as refrigerators, microwaves, air conditioning, and water heaters are connected to an IoT network and controlled remotely. Methods such as big data and data mining can be used to improve the efficiency of IoT and storage challenges of a large data volume and the transmission, analysis, and processing of the data volume on the IoT (Shadroo et al, 2018).

In the data mining field, if we do not analyze or extract any knowledge of these data, we can conclude that the generated data is worthless. In addition, the devices themselves can manage the data by using intelligent tools. Data mining techniques are used to analyze the data collected and enhance IoT intelligence (Kholod et al, 2016; Siddiqa et al, 2016; Chen et al, 2015).

Mining data from multiple data sources to extract useful information is considered to be a very challenging task in the field of data mining, especially in the current big data era. The methods of mining multiple data sources can be divided mainly into four groups: (i) pattern analysis, (ii) multiple data source classification, (iii) multiple data source clustering, and (iv) multiple data source fusion (Wang et al, 2018).

Reference

  • Chen, F. et al. (2015). “Data Mining for the Internet of Things: Literature Review and Challenges”. International Journal of Distributed Sensor Networks, vol, p. 14.
  • Gola, J., Britz, D., Staudt, Th., Winter, M., Schneider, A. S., Ludovici, M., Mücklich, (2018). “Advanced microstructure classification by data mining methods”. Computational Materials Science Volume 148, Pages 324-335.
  • Kholod, I., Kuprianov, M., Petukhov, I. (2016). “Distributed data mining based on actors for Internet of Things,” 5th Mediterranean Conference on Embedded Computing (MECO).
  • Roiger, R .J. (2017). “Data Mining: A Tutorial-Based Primer”. Taylor & Francis Group.
  • Shadroo, Sh., Rahmani, A. M. (2018). “Systematic survey of big data and data mining in internet of things”. Computer Networks, Volume 139, Pages 19-47.
  • Siddiqa, A., Hashem, I. A.T., Yaqoob, I., Marjani, M., Shamshirband, Sh., Gani, A., Nasaruddin, F. (2016). “A survey of big data management: Taxonomy and state-of-the-art”. Journal of Network and Computer Applications, vol. 71, pp. 151-166.
  • Wang, R., Ji, W., Liu, M., Wang, X., Weng, J., Deng, S., Gao, S., Yuan, C. (2018). “Review on mining data from multiple data sources”. Pattern Recognition Letters, Volume 109, Pages 120-128.

Back To Blog

No Comments

Post A Comment