Data Mining Tools and Data Mining Myths

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Table of Contents

Data Mining Tool
Data Mining Myths
References

Data Mining Tool

Data mining is a popular method of software analysis used to find patterns in large datasets. Today, various companies use this technology to build marketing strategies, manage credit risk, detect fraud, filter spam, or even define users’ sentiment. Today many specialists are concerned with the security and privacy problems that data mining evolves. There are two significant problems highlighted: data anonymization and validating external sources (Bhuiyan et al., 2018). Both issues are noted based on the different companies’ experiences.

The first problem is correlated with keeping the identity of the person evolved in data mining secret. The anonymization process contributes to a more reliable systematic analysis because the risk of data misuse is minimized. For example, this technique can be applied in hospitals and for insurance coding and billing (Bhuiyan et al., 2018). Even though there are various methods to prevent information fraud, the problem is substantial due to the existence of hackers who can implement de-anonymization techniques. The second issue is the validation of external sources, which the companies often overlook. This process requires many budget allocations and seems insignificant at the first glimpse. However, it is vital the ensure high-quality data protection. Therefore, the second problem is also substantiated by the irresponsibility of the stakeholders and companies’ administrators.

Data Mining Myths

One of the major myths regarding data mining is that it can replace domain knowledge. The market is unexpected, and without the domain expertise specialist, the company will have only a few chances to advance. Data mining tools provide the compression of data that a specialist should interpret (Raiker, 2019). Another myth is that only huge companies need to implement data mining. This tool can be efficiently used by any company disregarding of its status or size. The small amount of money can be efficiently used to analyze particular issues (Raiker, 2019). It is always much more convenient to work with the particular converted information rather than with the whole database.

References

Raiker, R. (2019). 5 myths of data mining. Medium. Web.

Shuiyan, M/. Jang-Jaccard, J., Qi, L., Liu, C., & Zhang, X. (2018). Privacy issues in big data mining infrastructure, platforms, and applications.Security and Communication Networks, 2018(2), 1–5. Web.

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Data Mining Tools and Data Mining Myths

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