Data Reduction | Dimensionality Reduction| PCA, Attribute Subset, Decision Trees | Lec. 05

In this lecture, we study Data Reduction, an important step in Data Preprocessing, which helps reduce data size while preserving essential information. This topic is crucial for handling large datasets efficiently in data warehousing and analytics. Data Preprocessing – Data Reduction - Dimensionality Reduction 📌 Topics Covered 🔹 What is Data Reduction? • Need for data reduction • Benefits in large-scale data 🔹 Techniques of Data Reduction • Principal Component Analysis (PCA) • Attribute Subset Selection • Decision Tree-based Reduction • Conceptual understanding with examples 🎯 Why this topic is important? Data reduction improves efficiency, performance, and scalability of data analysis. 🎯 Important for: GATE DA Data Preprocessing Data Analysis 📌 Helps in reducing dimensionality and removing irrelevant data. Reduce data → Improve performance → Better insights 🚀 #DataWarehousing #DataReduction #PCA #GATEDA