User:Hacksasaur/Data mining

Data mining, often considered a subset of knowledge discovery in databases (KDD), refers to the process of exploring large datasets to identify patterns, relationships, or any significant statistical correlations. Utilizing a blend of artificial intelligence, machine learning, and statistical techniques, data mining allows businesses and researchers to extract valuable insights and predict future trends. Its applications range from market segmentation, fraud detection, to predictive analysis. As data continues to grow exponentially in the digital age, data mining has become an indispensable tool for data-driven decision-making across various sectors.

Summary of Sources for Data Mining Edit
The current wiki article doesn't mention construction applications at all (aside from my earlier edit) so I decided it was a good place to expand. My first source is a scholarly article written by several experts in the field focusing on the types of major applications that data mining has in construction. Construction has begun to use data mining to help cut down the complexity of modern buildings to make it easier to plan. While knowing all the information is great, sorting the information into understandable pieces helps stakeholders understand the details. My second source focuses on construction costs and how to avoid or soften cost overruns. Often times construction projects go over budget because of either poor planning, poor management, or uncontrollable things like weather conditions. By optimizing for specific cases, costs can be more accurately determined and projects don't have to be cancelled. The main places I'll be focusing on is how exactly data mining is used, so that I don't stray too far from the main topic. I'll be using the first source as a general guideline for writing the expansion and use supporting sources to back the rest up as I go.

Construction Applications
As buildings grow more advanced, the data associated with maintaining safety, costs, and techniques has been growing exponentially. Given the complexity of construction planning, data mining has begun to simplify the work for construction stakeholders. Data mining helps to more accurately predict costs for all types of buildings and can reduce the likelihood of cost overruns. Factors about a project location, such as accessibility, ground conditions, weather, and available workers are used to determine costs that otherwise could not be defined. Information can also be gathered for other uses besides costs, like safety measures and material choices.

Peer Review
(I believe the rest of the review was likely a copy pasted template to help with formatting so I'm going to focus on just this section) "Suggestions for Improvement:


 * 1) Consider providing specific examples of data mining applications in construction to illustrate the points made.
 * 2) Include potential limitations or challenges of data mining in this sector to give a balanced view.
 * 3) Integrate internal Wikipedia links to related articles (e.g., link to articles about "predictive models," "cost estimation," or "safety regulations" in construction)."

My Response
I do think that adding specific examples of when people used data mining in the past to help with construction planning is a good idea. I'll need to do some additional reading but injecting some limitations such as increased time requirements and computing resources makes sense too. Lastly, while I appreciate the advice, I personally would prefer to keep articles out of my edit, because I'm worried that they'll affect the credibility of my writing. I'll gladly accept the other suggestions you gave me.