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unit 3/03_FP_Growth_Algorithm.md

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+# FP-Growth Algorithm
+
+**FP-Growth (Frequent Pattern Growth)** is a faster and more efficient algorithm than Apriori.
+
+## Why is it better?
+- **Apriori** scans the database many times (once for size 1, once for size 2, etc.). This is slow.
+- **FP-Growth** scans the database **only twice**.
+  1. First scan: Count frequencies.
+  2. Second scan: Build the **FP-Tree**.
+
+## The FP-Tree
+An **FP-Tree** (Frequent Pattern Tree) is a compressed tree structure that stores all the transaction information.
+- More frequent items are near the root (top).
+- Less frequent items are leaves (bottom).
+
+## Steps
+1. **Count Frequencies**: Find support for all items. Drop infrequent ones.
+2. **Sort**: Sort items in each transaction by frequency (Highest to Lowest).
+3. **Build Tree**: Insert transactions into the tree. Shared items share the same path.
+4. **Mine Tree**:
+   - Start from the bottom (least frequent item).
+   - Build a "Conditional Pattern Base" (all paths leading to that item).
+   - Construct a "Conditional FP-Tree".
+   - Recursively find frequent patterns.
+
+## Divide and Conquer
+FP-Growth uses a **Divide and Conquer** strategy. It breaks the problem into smaller sub-problems (conditional trees) rather than generating millions of candidate sets like Apriori.