Data mining is the process of uncovering information within a dataset; it is also known as Knowledge Discovery in Databases (KDD). There are 2 data mining results that you can achieve – describing the data you have or making predictions for the future.
The 1st and maybe the most difficult step in data mining is setting the business objective. It is also the most critical. If you don’t know what you are looking for, it will be tough to select the Machine Learning ML types, algorithms, and models to get the information you need.
Data mining can help with sales and marketing, so that a company better understands its customers and marketing. Schools and universities can use it to better understand their students based on information like time spent in a virtual classroom, the number of keystrokes, the classes students took simultaneously, or which classes have more successful test scores.
Corporations can also use data mining to optimize operations by understanding manufacturing, assembly, faults, and failures, among many things. It is also beneficial for fraud detection. Banking can use data mining to look for the patterns of fraud or even the store that has been compromised.
The 2nd step is to prepare your data. If you understand your objective, your data scientists can determine the relevant dataset so that the resulting information is useful for your business. Data scientists must clean the data, duplications, missing information, and outliers within this second step. All of this could prevent your data mining algorithms and tools from delivering the results you need.
The 3rd step is to build the model and mine for patterns. This is where the techniques and tools below come in. Data mining could use deep learning algorithms with supervised or unsupervised learning methods.
The fourth and final step is to evaluate the results the data mining has produced to make changes or take actions that are beneficial to the business.