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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: