1. Clustering
John Owen
Sarah Smith

2. What is clustering?
Grouping together objects that are like one another and not like the objects other clusters.
Like sorting laundry…

3. What is Clustering? Has its routes in statistical analysis
In data mining, clustering is used to give a user a high level view of what is going on in their database.

4. Clustering Approach Algorithms can be complex
The general approach contains five steps
Pattern representation
Identify the pattern proximity relative to the data domain
Grouping or Clustering of the data.
Data abstraction
Assessment of output.

5. Four Clustering Methods
Partitioning (k-means clustering)
Hierarchical
Density-Based
Grid-Based

6. Partitioning (k-means clustering)
Classification of the data into k groups, which meet two requirements
each group must contain at least one object, and each object must belong to exactly one group
The analyst decides how many clusters there should be, then creates the best fit of points to a cluster
The analyst must know the data to do this

7. Partitioning Example
(Source k-means clustering

8. Hierarchical Clustering
Analyst need not know the data
Designed primarily for creating micro-clusters in large database sets
Hierarchal method is either agglomerative (bottom-up) or divisive (top-down)

9. Hierarchical Example
(Source

10. Density-Based Clustering
Defines the data by the density of the data distribution
Does not require the user to identify the number of clusters before beginning the data analysis
Useful for dealing with outliers

11. Density-Based Examples
(Source:

12. Grid-Based Clustering
Adaptation of Density-Based Clustering
Data points are placed in a data grid
Each data grid is of equal size
Grids can be decomposed into smaller grids

13. Grid-Based Example

14. Business Uses of Clustering
Marketing
Identifying customers/clients who are outliers
Detection of Credit Card Fraud
Scientific inquiry
Human genome

15. Future of Clustering AI
Unsupervised Learning from pattern recognition