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Data mining is one of the most developing concepts contributing to sufficient data implementation within business activities. Advanced companies emerge with this strategy to efficiently analyze business-related information to strengthen their weaknesses and solidify their strengths (Mughal, 2018). However, as with any technological tool, data mining technologies can have errors or system mistakes. Moreover, without an efficient strategy, data mining becomes non-functional (Chandra and Gupta, 2020). The accuracy and results of the data mining process should be evaluated through unique algorithms.
One of the most efficient strategies to estimate and manage the information used for data mining is the implementation of the platforms for data processing. Such platforms as RapidMiner, Orange, and Weka can be efficiently used to estimate and adjust data mining procedures within one or several companies (Albayath et al., 2019). The researchers consider such an approach profitable because the mentioned platforms ensure a higher level of efficiency from the perspective of managemental algorithms, which can be used to structure and sort the data. Amazon company uses this approach to analyze and predict customers preferences based on their site activity (Agarwal, Kaul, and Raj, 2018). One of the limitations related to these platforms is the necessity to use cluster platforms (Ekici et al., 2018). It can disturb the initial structure hindering the standard data mining processing.
Another approach is related to estimating the classifier accuracy. This notion is central to understanding the process of data mining. All the activities are numerically described and collected based on the defined classifiers (Fawcett and Provost, 2013). The results and accuracy of data mining through classifiers can be estimated based on the cross-validation algorithm (Classifier accuracy measures in data mining, 2020). The complicated process of error identification is implemented in the techniques structure to ensure more accurate results (Huang et al., 2019). One example of an organization from the banking sector which implements this approach is Barclays bank. The online services of Barclays are built on the classifier structures (Yuan, 2021). It helps the bank to collect necessary information about the clients needs. Moreover, the error prevention mechanisms and general comfortable classifiers structure contribute to collecting data essential for advancing the quality of services. However, this method is not relevant for all organizations and industries (Yuan, 2021). The algorithms involving classifier structure need more investments and enhanced artificial neural networks, which is expensive. Small businesses are better focused on less complicated approaches.
Reference List
Agarwal, H., Kaul, P., and Raj, G. (2018) Effective prediction in amazon web service based clustered data using artificial neural networks, 2018 International Conference on Advances in Computing and Communication Engineering, pp. 207212.
Albayati, B. (2019) Evaluating the accuracy of data mining Algorithms for detecting fake Facebook Profiles using Rapidminer, Weka, and Orange, Journal of Theoretical and Applied Information Technology, 97(7), pp. 19371947. Web.
Classifier accuracy measures in data mining (2020) Web.
Ekici, G., et al. (2018) Big data techniques of Google, Amazon, Facebook and Twitter, The Educational Researcher, 13, pp. 94100.
Fawcett, T, and Provost, F. (2013) Data science for business. New York: Foster Provost and Tom Fawcett.
Gupta, M. and Chandra, P. (2020) A comprehensive survey of data mining, International Journal of Information Technology, 12, pp. 12431257.
Huang, J., et al. (2019) Improving IoT data quality in mobile crowd sensing: a cross validation approach, IEEE Internet of Things Journal, 6(3), pp. 56515664.
Mughal, J. (2018) Data mining: web data mining techniques, tools and algorithms, International Journal of Advanced Computer Science and Applications, 9(6), pp. 208215.
Yuan, R. (2021) Evaluation of the effect of pension model based on data mining, Annals of Operations Research, 1, pp. 112.
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