Data is very essential to knowledge discovery and decision-making today. However, organizations first have to subject raw data sets to several processes, transforming them into useful information. That’s where data mining comes in. The data mining concept has existed for a long time, mostly among seasoned data professionals. So, it’s tempting to see the term as a buzzword. This article delves deeper into data mining technology, revealing the numerous types of data mining.
What is data mining?
Data is a valuable asset to businesses of all sizes, and the volume of information produced each day is rated in quintillions. Generally, we produce more data than we can ever consume. However, these data sets collected from different sources may not be of good quality. Often, customers make mistakes during data entry, and employees forget to update specific records as they change in real time, leading to several data challenges.
Data mining helps data professionals take a closer look at their data sets, just like a gold miner will do with the raw gold dust before turning it in as pure, refined, cleaned, and usable. There are many different types of data mining, and the data mining process is also varied. Generally, it involves using a data mining tool or technique to transform relevant information for various uses.
Data mining is part of the data management process, which is an unending cycle with many data preparation steps including data analytics. Many people interchangeably use different terms, from data cleaning to data analytics, but it’s essential not to confuse the role data mining plays in the grand scheme of things.
The overarching aim of data mining is to identify patterns in large volumes of data in your warehouse. The output from the data mining process serves as input for the data analytics stage, and there are many different data mining techniques for data professionals to leverage.
What are the different types of data mining?
The various types of data mining techniques are often categorized into several other subtypes. Some of them are described below.
Classification
Classification is a predictive data mining technique. It helps businesses predict the values of categorical and continuous target variables accurately. In simple terms, this data mining method helps classify data sets, enabling businesses to search and find relevant information quickly. It eliminates a very key problem in the data industry. According to IBM, about 19.8 percent of business time is wasted finding the right information for effective work. That translates into one day out of every working week.
This data method uses two steps. The training step involves using a data mining algorithm to train data sets while the classification step estimates the accuracy of business rules for the data mining process.
Regression
Regression comes in two forms: linear and logistic. Linear regressions help businesses predict continuous variables using several independent inputs. Linear regressions are a mainstay in the real estate industry, helping realtors predict home values with variables like ZIP code location and year of construction.
In contrast, logistic regressions predict the probability of categorical values, often used in banking systems to determine the chances of loan applicants defaulting. The banks can use variables like credit score, income levels, gender, and other factors. Generally, this data mining technique aids accurate prediction and forecasting.
Clustering
Clustering’s concept is similar to the classification data mining technique. Some key clustering methods include density-based modeling and hierarchial agglomerative methods.
Neural Networks
This data mining technique hinges on collecting neurons and establishing the connections between them. Neural networks model the relationship between inputs and outputs, designed to work just like the brain.
All in all, as a part of data management, data mining helps businesses learn more about their customers, developing efficient business strategies tailored to their needs. Businesses need to identify the types of data mining that best suit their problems and organizational goals.
