What is Cluster Analysis? Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups

Applications of Cluster Analysis Understanding Group related documents for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations Summarization Reduce the size of large data sets Clustering precipitation in Australia

Notion of a Cluster can be Ambiguous How many clusters? Six Clusters Two Clusters Four Clusters

Types of Clusterings A clustering is a set of clusters Important distinction between hierarchical and partitional sets of clusters Partitional Clustering A division of data objects into non-overlapping subsets (clusters) such that each data object is in exactly one subset Hierarchical clustering A set of nested clusters organized as a hierarchical tree

Partitional vs Hierarchical Clustering Original Points A Partitional Clustering Dendrogram Hierarchical Clustering

Other Distinctions Between Sets of Clusters Exclusive versus Overlapping versus Fuzzy Exclusive: each object is assigned to only one cluster Overlapping: an object may belong to more than one cluster Fuzzy: every object belongs to every cluster, with a weight between 0 (absolutely doesn’t belong) and 1 (absolutely belongs) Partial versus complete Complete clustering: assigns every object to a cluster Partial clustering: does not have to assign every object to some clusters

K-means Clustering Partitional clustering approach Each cluster is associated with a centroid (center) Number of clusters, K, must be specified

Illustrating K-means

Illustrating K-means

K-means Clustering – Details Initial centroids are often chosen randomly. Clusters produced vary from one run to another. ‘Closeness’ is measured by a proximity measure e.g., Euclidean distance, cosine similarity, or correlation K-means will converge for common proximity measures mentioned above. Most of the convergence happens in the first few iterations. Complexity is O( n * K * I * d ) n = number of points, K = number of clusters, I = number of iterations, d = number of attributes

K-means as Optimization Problem k-means clustering can be viewed as an iterative approach for minimizing sum of squared error (SSE) x: a data point in cluster Ci i is the representative point for cluster Ci For SSE, we can show that i corresponds to center of the cluster One easy way to reduce SSE is to increase K, the number of clusters A good clustering with smaller K can have a lower SSE than a poor clustering with higher K

Two different K-means Clusterings Original Points Optimal Clustering Sub-optimal Clustering

Importance of Choosing Initial Centroids …

Importance of Choosing Initial Centroids …

10 Clusters Example Starting with two initial centroids in one cluster of each pair of clusters

10 Clusters Example Starting with two initial centroids in one cluster of each pair of clusters

10 Clusters Example Starting with some pairs of clusters having three initial centroids, while other have only one.

10 Clusters Example Starting with some pairs of clusters having three initial centroids, while other have only one.

Solutions to Initial Centroids Problem Multiple runs Helps, but probability might not be on your side Sample and use hierarchical clustering to determine initial centroids Select more than k initial centroids and then select among these initial centroids Select the most widely separated centroids Postprocessing Bisecting K-means

Empty Clusters K-means can yield empty clusters Empty Cluster

Handling Empty Clusters Choose a new centroid to replace the empty cluster Several strategies Choose the point that contributes most to SSE this corresponds to the point that is farthest away from any of the current centroids Choose a point from the cluster with the highest SSE This will split the cluster and reduces the overall SSE

Pre-processing and Post-processing Normalize the data Eliminate outliers Post-processing Eliminate small clusters that may represent outliers Split ‘loose’ clusters, i.e., clusters with relatively high SSE Merge clusters that are ‘close’ and that have relatively low SSE

Bisecting K-means algorithm Variant of K-means that can produce a partitional or a hierarchical clustering

Bisecting K-means Example

Limitations of K-means K-means has problems when clusters are of differing Sizes Densities Non-globular shapes K-means has problems when the data contains outliers.

Limitations of K-means: Differing Sizes Original Points K-means (3 Clusters)

Limitations of K-means: Differing Density Original Points K-means (3 Clusters)

Limitations of K-means: Non-globular Shapes Original Points K-means (2 Clusters)

Overcoming K-means Limitations Original Points K-means Clusters One solution is to use many clusters. Find parts of clusters, but need to put together.

Overcoming K-means Limitations Original Points K-means Clusters

Overcoming K-means Limitations Original Points K-means Clusters