Data mining is the process of discovering meaningful patterns in large datasets to help guide an organization’s decision-making. With the use of techniques like regression, classification, and cluster analysis, data mining can sort through vast amounts of raw data to analyze customer preferences, detect fraudulent transactions, or perform social network analyses. Data mining is important because it delivers the descriptive and predictive analytics needed by an organization to increase productivity and sales, reduce costs, and prepare for the future.
Like other areas of data science, data mining typically relies on the Python programming language for tasks like data cleansing, data organization, and machine learning (ML) applications. In social data mining, data clustering algorithms are used to inform recommender systems that can guide customers in entertainment and e-commerce choices. When delving into unstructured datasets, data mining can employ information retrieval (IR) and natural language processing (NLP) for text mining applications that can uncover customers’ emerging concerns or unmet needs.