Data Mining Assignment Part 2: Techniques, Applications, and Analysis

This assignment presents a discussion board post analyzing the application of data mining techniques. The student discusses the importance of data credibility, the use of decision trees, and the relevance of data mining in project management, drawing on real-world examples from oil pipe trenching. The post also addresses the applicability of various data mining techniques, including sequential patterns. The assignment is a response to a prompt requiring the student to identify a problem amenable to data mining, describe the data, potential benefits, questions to be answered, relevant techniques, and irrelevant techniques. The student provides insights into how data mining can be used to improve efficiency and decision-making in various fields, referencing relevant literature to support their points.

Running head: DATA MINING ASSIGNMENT PART 2_GREGORYEZEANI
DATA MINING ASSIGNMENT PART 2_GREGORYEZEANI
Name of Student
Name of University
Author Note

1DATA MINING ASSIGNMENT PART 2_GREGORYEZEANI
Hi Gregory,
Your post was very insightful about just how much data based actions have revolutionized how
things are done. You explained that as an engineer for oil pipe trenching how important data was
to your project. It just goes to show how important data has become and how far the scope is of
data driven decision making has become in today’s world. You mentioned that data credibility
and verifying the source of your data was something that you had checked rigorously. That is a
very fundamental and important aspect when dealing with data science and data mining,
especially when the stakes are so high (Zhao, 2015). Data mining is I am sure you would agree is
not only a science but an art which requires keen attention to detail as well as understanding of
the domain. Decision trees as you have said that you make use of so frequently is a tool with vast
applicability which also has quite a scope of use in terms of marketing (Ahmed, Ahmed &
Mckay, 2014). You also mentioned that you do not find sequential pattern technique to not be
that useful, however it is has considerable role to play in marketing and setting market strategies
(Witten et al., 2016). Thus it is to be noted how vast the scope of data mining is and the range of
specific techniques it is comprised of. Nonetheless, as Ertek et al.(2017) said, data mining is
essential for project management today for the invaluable insights it can provide for decision
makers for not only forming contingency plans but also for real time decision processes as you
have discussed in your post.

2DATA MINING ASSIGNMENT PART 2_GREGORYEZEANI
References
Ahmed, K. J., Ahmed, M. K., & McKay, S. (2015). A brief review of alternative uses of data
mining: Education, engineering, & others.
Ali, S. M., &Tuteja, M. R. (2014). Data Mining Techniques.
Ertek, G., Tunc, M. M., Zhang, A. N., Tanrikulu, O., & Asian, S. (2017, December). Data
Mining of Project Management Data: An Analysis of Applied Research Studies.
In Proceedings of the 2017 International Conference on Information Technology(pp. 35-
41). ACM.
Sarstedt, M., &Mooi, E. (2014). Cluster analysis. In A concise guide to market research (pp.
273-324). Springer, Berlin, Heidelberg.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.
Zhao, Y. (2015). Data mining techniques