Data Mining || Supervised vs. Unsupervised Techniques || Dimensionality Reduction || Partitioning Methods
Data Mining Need for Data Mining Data mining is the process of extracting meaningful patterns, trends, and knowledge from large datasets. Its need arises from: Data Explosion : Organizations generate massive amounts of data that need to be analyzed effectively. Decision-Making : It supports informed decision-making by identifying hidden patterns and correlations. Competitive Advantage : Helps businesses optimize processes, enhance customer relationships, and forecast trends. Automation : Reduces manual data analysis efforts and increases efficiency. Problem-Solving : Detects anomalies, predicts outcomes, and aids in problem-solving across domains. Data Mining Tasks Classification : Assigning items to predefined categories (e.g., spam email detection). Clustering : Grouping similar data points together without predefined labels (e.g., customer segmentation). Regression : Predicting ...
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