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.
Data mining techniques allow data scientists and businesses to make better use of large amounts of data. Some of the techniques include:
- Pattern tracking is a fundamental technique of identifying patterns, such as the increased sales of snow shovels when a storm is coming, for example. Except what you are looking for are things that are not so obvious.
- Classification is another technique that allows data to be put into different categories and assigned a classification. For example, you can classify bank customers based on their financial history as low, medium, or high-risk credit card customers.
- Association is another method related to pattern tracking. It looks for variables linked at specific moments. An example would be the realization that a sauce choice will come next when a customer puts pasta into their shopping cart. Or that after the sauce, there will be parmesan cheese.
- Outlier detection is another method of data mining that looks for exceptions or anomalies. An example would be a massive spike in sales to female customers in a typically male store in June because, as it turns out, women shop for the fathers the week or two before Father's day.
- Clustering is another technique that is similar in nature to the classification technique. This time, data is grouped because of its similarity. Customers could be linked by shopping frequency or disposable income.
- Regression is the ability to predict a value based on values in the past. Regression looks for the average over time, because things like home prices will fluctuate a little high to a little low over time compared to the current average price.
- Prediction is a data mining technique that allows businesses to forecast a value in the future.
Data mining tools are essential to improving the effect data mining has on the productivity of the company. Some of the top tools out today are:
- RapidMiner Studio
- Sisense for Cloud Data Teams
- Alteryx Designer
- Qlik Sense
MonkeyLearn is a text analysis tool. You can use it to detect sentiments like negative online reviews or automate your ticket tagging and routing processes.
RapidMiner Studio is an open-source platform that offers a drag-and-drop interface that allows non-programmers to customize their use case. It can be used for fraud detection or customer turnover. For programmers, there are R and Python extensions that customize the data mining. There is also a terrific community for support.
Sisense for Cloud Data Teams allows teams to work together to extract intelligence from their data no matter the technical level of the team member.
Alteryx Designer makes it possible for a data analysts to prepare, blend, and analyze their data with one tool.
Qlik Sense is a visualization software tool with "stunning charts and graphs." It allows multiple data sources to be analyzed with drag and drop functionality.