• 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

Divisive Hierarchical Clustering

Divisive clustering Divisive clustering (also called top-down hierarchical clustering) is the inverse of agglomerative clustering. Instead of starting with individual data points and merging them, divisive clustering starts with all data points in a single cluster and recursively splits them until certain criteria are met (e.g., a desired number of clusters is formed). It builds a tree (dendrogram) from top to bottom. Working of Divisive Clustering

• Start with all data points in one cluster.
• At each step:
• Choose a cluster to split (usually the one with the highest dissimilarity).
• Use a flat clustering algorithm (like K-Means or spectral clustering) to split it into two subclusters.
• Repeat recursively for each subcluster until:
• A stopping condition is met (e.g., number of clusters = k or minimum cluster size).