In AI, data mining is the process of uncovering patterns and insights from large datasets. It uses statistical and mathematical methods to analyze data and find relationships, trends, and anomalies. The goal is to extract valuable information and make informed decisions.
Data mining algorithms include techniques like association rule mining, clustering, classification, and anomaly detection. It has wide-ranging applications in industries like finance, healthcare, retail, and marketing. For instance, data mining can help detect fraud and analyze customer behavior.
Data mining requires a significant amount of data and computing power, as well as an understanding of both the data and the business goals. Despite these challenges, the insights gained from data mining can lead to significant improvements for organizations.
Data scraping, often known as web scraping, is the process of extracting information from websites and storing it in files or spreadsheets.
Parsing is the process of analyzing and breaking down a sentence, phrase, or other form of natural language into its component parts, with the goal of understanding the intended meaning.
Big data refers to vast and complicated data sets that are created and collected in real time from multiple sources.