• 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

Frequent Pattern Growth

Frequent Pattern Growth (FP-Growth) in Association Analysis

FP-Growth is an advanced and efficient algorithm for frequent itemset mining, commonly used in association rule learning. It addresses the performance limitations of the Apriori algorithm by avoiding costly candidate generation and repeated database scans.

FP-Growth stands for Frequent Pattern Growth. It finds frequent itemsets directly from the database using a special structure called an FP-tree (Frequent Pattern Tree).

• Step 1: Build the FP-Tree
  • Scan the database once to determine item frequencies.
  • Sort items in descending order of frequency.
  • Build a compressed tree structure (FP-tree) where paths represent itemsets.
  • Items shared by multiple transactions share nodes (saves space!).
• Step 2: Extract Frequent Patterns
  • Start from the bottom of the tree, find frequent items and grow patterns recursively.
  • Use conditional FP-trees to mine larger patterns without candidate generation.