Birch is an iterative clustering algorithm that builds a CF (Clustering Feature) tree to group similar data points together in clusters. It performs hierarchical clustering without scanning the entire database by making clustering decisions based on the CF tree. The algorithm has four phases - it first builds an initial CF tree, then condenses it, performs global clustering on the leaf nodes, and optionally refines clusters through additional scans. Birch aims to minimize data scans and running time for very large databases by exploiting non-uniform data distributions.

1. Birch Algorithm
Presented by:
Binod Malla
09BIM012

2. Introduction
•Balance
•Iterative
•Reducing &
•Clustering using
•Hierarchies

3. Definition
• Effective data clustering method for very large databases
• Each clustering decision is made without scanning all data points
• unsupervised data mining algorithm used to perform hierarchical
clustering

4. Data Clustering
• It is portioning of a database into clusters
• Closely packed group
• Collection of data objects that are similar to one another & treated
collectively in a group

5. Clustering Factor CF
• CF is compact storage for data on points in cluster
• Has enough information to calculate the intra-cluster distances
• Additive theorem allows to merge sub-clusters

6. BIRCH goal
• Minimizes running time and data scans
• Clustering decision made without scanning whole data
• Exploit the non uniformity of the data
• Treat dense areas as one and reduce noise

7. Algorithm
• Phase 1: Scan all data and build an initial in-memory CF tree, using
the given amount of memory and recycling space on disk.
• Phase 2: Condense into desirable length by building a smaller CF tree.
• Phase 3: Global clustering.
• Phase 4: Cluster refining – this is optional, and requires more passes
over the data to refine the results.

8. Birch- Phase 1
• Starts with initial threshold and inserts points into the tree.
• If it runs out of memory before it finishes scanning the data,
it increases the threshold value and rebuilds a new, smaller CF tree,
by re-inserting the leaf entries from the older tree and then other values
• Good initial threshold is important but hard to figure out.
• Outlier removal (when rebuilding tree).

9. Birch- Phase2
• Optional
• Preparation for phase 3
• Removes outliers and grouping clusters

10. Birch- Phase3
Problem after phase 1
• Input order affects results.
• Splitting triggered by node size.
Phase 3
• Cluster all leaf node on the CF values according to an existing
algorithm
• Algorithm used here: agglomerative hierarchical clustering

11. Birch-Phase4
• Optional
• Additional scans of the datasets attaching each items to the centroids
found.
• Recalculating the centroid and redistributing the items
• Always converges

12. Conclusion
• Birch performs faster than existing algorithms
(CLARANS and KMEANS) on large databases.
• Scans whole data only once.
• Handles outliers better.
• Superior to other algorithms in stability and scalability.