Data Mining Techniques in Analyzing Process
Read this article and pay attention to the data mining techniques, classifier development, and evaluation criteria. Then take notes and understand the difference between supervised and unsupervised learning models. Finally, read the summary and discussion section of this article. What distinctions can be made about the three major purposes of problem-solving items using data-mining techniques?
There are different types of data warehouses, and each has a specific purpose within an organization. Remember, it is important to use the correct type of warehouse to support the "decision support" model being employed. Decision support techniques such as classification, prediction, time-series analysis, association, clustering, and so on will each have their own unique data needs. Correctly designing the data warehouse will ensure the best possible evidence to support strategic and daily decisions.
Managing data is an important function in the administrative process. Because organizations use data to guide decisions, decision-makers rely on you to produce a data management plan for sustainability, growth, and strategy. As you start to interact with decision-makers and the decision-support systems they use, you will also find that additional study of the models employed through a course on quantitative methods or decision-support technology will prove useful.
Methods
Participants
The USA sample (N = 429) was extracted
from the 2012 PISA public dataset. Students were from 15 years 3 months
old to 16 years 2 months old, representing 15-year-olds in USA. Three
students with missing student IDs and school IDs were deleted, yielding a
sample of 426 students. There were no missing responses. The dataset
was randomly partitioned into a training dataset (n = 320, 75.12%) and a
test dataset (n = 106, 24.88%). The size of the training dataset is
usually about 2 to 3 times of the size of the test dataset to increase
the precision in prediction.