• Introduction
• MultiDimensional Data Model
• Association Analysis
• Data Warehouse Architecture
• Capacity Planning
• Q and A
• Search Engine
• Clustering_Code
• Practical Questions
• Outlier finding Clustering Algorithm
• k-means clustering algorithm
• k-medoids clustering algorithm
• k-means vs k-medoids
• DBSCAN
• Divisive Hierarchical Clustering
• Agglomerative hierarchical Clustering
• Limitations of Data Mining
• Predictive and Descriptive Data Mining
• Decision Tree
• Bayesian Classifier
• Association Analysis Algorithms
• Frequent Pattern Growth
• Star Schema
• Data Cube
• OLAP operations
• Linear Classification
• Reconstruction-based Outlier Detection
• Reconstruction-based Outlier Detection using Auto Encoder
• Q_and_A_2025_new
• Data warehouse - Example - i
• Simple - Apriori- First step
• Bayesian Classifier - Concept

Data Mining And Data Warehousing

Practical Questions

Write a program (or use a tool) to detect outliers in a dataset using methods like: Z-Score, IQR (Interquartile Range)

Write a program (or use a tool like MS Excel or Python) to: Handle missing values Perform normalization and standardization Display the summary statistics of a dataset

Find distance from point A(33,22,39) to points B(28,20,10), C(30,40,20) and D(33,21,30). Which one is closet to A.

Write a program to calculate information gain, make your own dataset.

Write a program to find class of object using Bayesian Classifier. Make your own dataset.

Write a program to find count of two-itemsets

Write a program to find core point, given min_pts=5 and epsilon-distance = 5.

Generate different summary tables using pivot table, use your own dataset.

Consider a dataset, then transform integer value attributes to range (0,1) using min-max normalization method. and z-score.