Data mining is the process of discovering patterns by analyzing large amounts of data. Data mining often uses machine learning in order to perform predictive analytics of big data. This type of analysis utilizes the available data to discover patterns which can then be used to predict future trends and outcomes. Data mining can be performed from a variety of sources, including data warehouses and data lakes, as well as from different types of data, both processed and raw data. While manual extraction of patterns has been around for many years, it has been revolutionized and expedited through the use of new technologies. Data science methods for machine learning, such as cluster analysis, decision trees, and neural networks, have been used to analyze big data that would be too large for manual analysis.
Typically, data scientists will set up systems and processes that allow for analyses to be run in real time as part of data collection. The set of data that is being analyzed can come from almost anywhere, such as utilizing information from a search engine, what is being sold at any given time, or even what crimes are being reported and where. Data mining techniques can take this data and use it for predictive modeling, which can be used not only to predict what may happen in the future, but also to help identify suspicious activity as it is occurring.
Data mining ensures that collected data is being utilized rather than wasted. Data collection by itself doesn’t help businesses and organizations. Instead, it takes proper analysis to make use of this data. Machine learning processes enable organizations to take the data they have and figure out relevant trends on which they can capitalize.
Data mining can be useful for multiple industries in different ways, some of which include:
- Retail sales: Retail locations can utilize what is known as a “market basket analysis” to determine what products are most often purchased together, and then figure out how best to market the items, whether in sales advertisements or by arranging their placement in store.
- Medical diagnoses: Data mining and analysis can be used to support medical diagnoses. By looking at large data sets around particular diseases or infections, machine learning can help identify individuals whose inputted health data is read as anomalous. This can help lower the amount of time that physicians have to spend on testing and can speed along a diagnosis.
- Security breaches: By having a model that shows what a typical interaction with a website looks like for a user, data mining can identify interactions that are far outside of the norm. This allows for a near immediate investigation into any activity deemed suspicious so that appropriate actions, such as disabling accounts, can occur.
- Disease management: Data mining can help identify disease hotspots based on inputted data around the locations of infected individuals. This can help identify other individuals who are potentially infected, as well as enabling a closer look into the areas that spread disease, so proper mitigation techniques can be utilized.