Data Warehouse MAKAUT

Here are all the questions and concepts mentioned in the video:

1. Introduction to Data Warehouse and Data Mining (PEC-IT602B)

   • DW, DM
   • Star, Snowflake, fact schema, sum
   • OLAP, OLTP
   • ETL Process
   • KDD
   • Data Mart
   • Data Pre-processing
   • Types of Attributes
   • Numericals -> min max
   • z score Normalization
   • Data Discretization
   • Data Wrangling
   • Data mining techniques

2. Classification & Clustering Analysis

   • Classification problems -> Naive Bayes + sums
   • Decision Tree + sums
   • various types of Distance measures.
   • Euclidean
   • Manhattan
   • Cosine Similarity
   • Jaccard Similarity
   • Clustering problems -> K means
   • K medoid
   • PAMs
   • Hierarchical -> Agglomerative Algo + Sums
   • Divisive Algo + Sums
   • CLARA, CLARANS

3. Mining Time Series Data

   • Time series Data
   • Components of Time series -> Trend (T), seasonal variations (S), cyclic variations (C), random movement (I)
   • Models of Time Series Analysis
   • Additive model (O=T+S+C+I)
   • Multiplicative Model (O=T*S*C*I)
   • Decision tree & its Construction Principle
   • Pearson correlation & Bayesian Classification

4. Mining Data Streams

   • Apriori Algo + Sums
   • Frequent Pattern Mining + sums
   • Market Basket Analysis
   • Class Imbalance Problem -> ensemble learning -> Bagging, Boosting and Stacking
   • Association Rule
   • Information Gain & Gain Ratio
   • Tree Pruning Techniques
   • ROLAP, MOLAP, HOLAP
   • Splitting Attributes
   • Synopsis & Synopsis D.S in Stream Data Mining
   • Histogram
   • Quantile
   • Sketches
   • Stream Data Processing Technique -> Reservoir Sampling, Sliding window model

5. Web Mining

   • Web Mining & its types -> Content mining, Structure, Usage
   • Web Crawler
   • Web Logs
   • Page Rank Algo
   • Distributed Data Mining

6. Recent Trends in DNDM

   • Graph Mining
   • SNA (Social Network Analysis)
   • DSMS (Data Stream Management System)