Lecture 17 final question bank with 100 MCQs, 30 short questions, and 15 long questions for Data Mining. Exam-focused, solved, and syllabus-aligned. SECTION A – MCQs (WITH ANSWERS) ANSWER KEY (MCQs) 1–20: B, C, B, C, A, D, B, B,…
Lecture 16 explores how robotics and automation use Data Mining for perception, navigation, motion planning, intelligent decision-making, sensor analysis, and Industry 4.0 applications. Includes ML, SLAM, RPA, and dataset references. Robotics has evolved from mechanical systems performing basic movements into…
Lecture 15 explains Big Data Analytics for Data Mining, including Hadoop, Spark, HDFS, MapReduce, distributed storage, real-time processing, MLlib, large-scale clustering, classification, and industry case studies. As organizations generate enormous volumes of data from sensors, mobile apps, IoT devices, social…
Lecture 14 explains time series data mining, sequential pattern algorithms, forecasting models (ARIMA, LSTM), preprocessing, similarity measures, anomaly detection, and real-world applications in finance, healthcare, IoT, and cybersecurity. Time-series data is everywhere stock markets, weather sensors, medical signals, website traffic,…
Lecture 13 provides a complete guide to Deep Learning for Data Mining, covering neural network foundations, CNNs, RNNs, LSTMs, Autoencoders, feature learning, backpropagation, training techniques, real-world applications, and case studies. Deep Learning represents one of the most powerful and transformative…
Lecture 12 explains Data Warehousing & OLAP for Data Mining, covering architecture, ETL processes, star/snowflake schema, multidimensional models, OLAP operations, cubes, case studies, and real-world applications. Data Mining is only effective when the underlying data is integrated, clean, consistent, and…