Data mining

With global industries, more digital landscapes and enormous quantities of data holding valuable information, data mining is becoming increasingly important. We all know that our data is valuable, but how can organizations tap into that data to gain actionable insights, prevent issues before they occur and make informed business decisions? Join Micron as we discover what data mining is and how it can be applied across industries.

What is data mining?

Data mining definition: Data mining is the process of analyzing large quantities of data, identifying patterns and classifications, and using the results to understand the dataset or generate new data.

Data mining builds on data warehousing, using machine learning and data classification techniques to “mine” information from stored data. It is a critical component of knowledge discovery in databases, or KDD, emphasizing the extraction of valuable information from large datasets.

Data mining is typically used in one of two ways: to classify and describe the data or to make predictions. In both applications, data goes through a systematic process of organizing, filtering, sorting and analyzing. Often, data is passed through ​​machine learning algorithms to enhance this process.

How does data mining work?

Data mining is designed to extract information, predictions, analysis, and classifications from a large quantity of data. To do so, it uses algorithms to identify trends and patterns within the data. With these patterns, data can be classified into categories, analyzed and even used to make predictions. 

There are four core steps in data mining: 

• Data gathering: Locating and gathering the data that a project will mine differs between organizations and projects. It may come from different sources, be located in analytics systems, or be obtainable from a data warehouse. 
• Data preparation: Data from different sources often needs to be aligned in format for consistent analysis. This step is also when data is cleaned up and errors are resolved. All this cleanup ensures a consistent, functional dataset. 
• Data mining: Data scientists run the data through one or more algorithms to mine the dataset. The process for selecting the data mining method differs depending on the desired result and project resources. 
• Data analysis: Results of the data mining process are analyzed thoroughly to maximize the exploration. Data scientists can present findings and draw conclusions with the classified and analyzed data. 

What is the history of data mining?

Data mining was originally a manual process, with people analyzing information for insights. As a manual process, it predates the ​artificial intelligence (AI) and machine learning techniques that now underpin it. 

• 1960s, automation: Originally it was a manual process, automated processes were developed after. The basis of data mining began with the advent of advanced computing in the 1960s. 
• 1989, KDD: Knowledge discovery in databases was pioneered in the late 1980s, contributing to data mining as an automated computational process. 
• 1990s, data mining development: In the 1990s, following the emergence of data analytics technologies and developments in machine learning, data mining gained traction as a technical innovation. 
• Late 1990s, development of scholarship and interest: In 1995, Montreal hosted the first international conference on knowledge discovery and data mining, marking the growth of academic interest in data mining. 
• 2010 onwards, increased reliance on data mining: Since the 2010s, artificial intelligence has rapidly become a fundamental part of major technologies. This evolution has made data mining increasingly important as well. With streamlined, automated processes for classifying, analyzing, and storing large amounts of data, artificial intelligence technologies can work more efficiently and effectively. 

What are the key types of data mining?

Data mining can typically be defined as descriptive or predictive based on the different aims a data mining project may have. But there are also other types of data mining: 

• Anomaly detection identifies unusual data within a dataset. 
• Association rule learning explores the relationships between individual pieces of data to better understand and contextualize them. 
• Clustering groups data based on the data alone, without context. 
• Classification takes input labels and applies them to the data to categorize it. 
• Regression creates labels based on what it can understand from the data. 
• Summarization condenses the data into the core information.

How is data mining used?

Data mining can be applied across industries. With a wide range of industries now relying on complex global connections, large quantities of data are fundamental to businesses of all kinds and require careful handling and analysis. 

One key case for data mining is the use of sensors to detect anomalies through classification. With all the data neatly classified, data mining expedites, automates, and ensures accuracy in checking for mistakes, errors, and anomalies. This anomalous data stands out and can be easily identified and/or removed, streamlining and enhancing data analysis. Highlighting anomalous data can help find fraudulent data, identify bots on social media sites, or pinpoint product defects. 

Data mining can also build models and identify risks, defects, and potential issues. For example, in the financial services industry, data mining tools are used to identify security risks and detect fraud. The same principles can be applied to cybersecurity where real-life risks can be identified through representative data. 

Data mining can also detect risks, issues, and power innovation in the automotive industry. In fact, it is a crucial step in training advanced driver-assistance systems (ADAS), a growing area of interest in the autonomous vehicle field.