The Role of Data Mining
Data Mining Techniques: Review
According to Osman, “data mining is the process of examining a pre-existing database for the purpose of generating new information” (Osman, 2019). The role of data mining in an organization is to identify and predict future trends, making the business more proactive and better knowledge-driven decisions. The unknown data is detected using data mining techniques and tools to improve decision-making processes. Data mining techniques, such as association, are used in organizations to understand customers’ purchasing behavior.
The classification technique provides for classes or groups of data to make accurate predictions. Classification is detrimental because it allows for relevant data about customers’ age, gender, and other demographic factors that can inform businesses on what to produce for a specific target audience and the returns. The output is also predicted sin data mining tools. Data mining can also be used to move the business forward because historical data is analyzed to identify interesting patterns.
“Application of Data Mining Techniques for Medical Data Classification: A Review”
Data mining has been integrated into customer relationship management, engineering, marketing, and medical analysis. In healthcare, data mining has improved decision making through the patterns and trends discovered in large sets of a complex database as opposed to how clinical decisions were made based on intuition or experience, (Lashari et al., 2018). Data today in the medical field is enormous and complex; therefore, the article posits that data mining tools and techniques are effective because it’s user-friendly and past experiences are ground for predictions. Associations in data from the past and current or the new data are revealed to inform the decisions most clinicians undertake. The authors claim that to deploy data mining algorithms to medical data, researchers need to clarify the types and functions of the data mining algorithms.
References
Lashari, S. A., Ibrahim, R., Senan, N., & Taujuddin, N. S. A. M. (2018). Application of Data Mining techniques for medical data classification: A review. In MATEC Web of Conferences (Vol. 150, p. 06003). EDP Sciences. Retrieved from https://www.matec-conferences.org/articles/matecconf/pdf/2018/09/matecconf_mucet2018_06003.pdf
Osman, A. S. (2019). Data Mining Techniques. International Journal of Data Science Research, 2(1), 1-5. Retrieved from http://ojs.mediu.edu.my/index.php/IJDSR/article/view/1841/717
Response to Post 1
I agree that data mining is a process used to extract data from large data sets and analysis based on trends or patterns. It is a set of processes and algorithms which are designed to generate insights and identify relationships that exist in the datasets (Plotnikova et al., 2020). Complexity in datasets in organizations can be simplified using data mining tools such as classification, association, and clustering. KDD is vital in data mining because it puts data storage and access, interpretation, and visualization of results into consideration.
Response to Post 2
I agree that data mining is used to predict market trends and study the hidden patterns in complex datasets. Data mining is applicable in all fields, including medicine, engineering, and business is the leading. Data mining offers an opportunity to identify complex relationships within sets of data, and comparisons are also made during this process (Mellor et al., 2018). The main objective of integrating data mining tools is to extract meaningful information used in decision-making processes and track performance in organizations based on past historical data.
References
Mellor, J. C., Stone, M. A., & Keane, J. (2018). Application of data mining to “big data” acquired in audiology: Principles and potential. Trends in Hearing, 22, 2331216518776817. Retrieved from https://journals.sagepub.com/doi/full/10.1177/2331216518776817
Plotnikova, V., Dumas, M., & Milani, F. (2020). Adaptations of data mining methodologies: a systematic literature review. PeerJ Computer Science, 6, e267. Retrieved from https://peerj.com/articles/cs-267/