1. Clustering I

2. 2 The Task Input: Collection of instances –No special class label attribute! Output: Clusters (Groups) of instances where members of a cluster are more ‘similar’ to each other than they are to members of another cluster –Similarity between instances need to be defined Because there are no predefined classes to predict, the task is to find useful groups of instances

3. 3 Example – Iris Data

4. 4 Example Clusters? Petal Length Petal Width

5. 5 Different Types of Clusters Clustering is not an end in itself –Only a means to an end defined by the goals of the parent application –A garment manufacturer might be looking for grouping people into different sizes and shapes (segmentation) –A biologist might be looking for gene groups with similar functions (natural groupings) Application dependent notions of clusters possible This means, you need different clustering techniques to find different types of clusters Also you need to learn to find a clustering technique to match your objective –Sometimes an ideal match is not possible; therefore you adapt the existing –Sometimes, a wrongly selected technique might show unexpected clusters – which is what data mining is all about

6. 6 Cluster Representations Several representations are possible a k j h d g i f e c b Partitioning 2D space showing instances e c b 123 a0.40.10.5 b0.10.80.1 c0.3 0.4 d0.1 0.8 f i g h d a j k Venn Diagram Table of Cluster Probabilities abcdef g Dendrogram

7. 7 Several Techniques Partitioning Methods –K-means and k-medoids Hierarchical Methods –Agglomerative Density-based Methods –DBSCAN

8. 8 K-means Clustering Input: the number of clusters K and the collection of n instances Output: a set of k clusters that minimizes the squared error criterion Method: –Arbitrarily choose k instances as the initial cluster centers –Repeat (Re)assign each instance to the cluster to which the instance is the most similar, based on the mean value of the instances in the cluster Update cluster means (compute mean value of the instances for each cluster) –Until no change in the assignment Squared Error Criterion –E = ∑ i=1 k ∑ pЄCi |p-m i | 2 –where mi are the cluster means and p are points in clusters

9. 9 Interactive Demo http://home.dei.polimi.it/matteucc/Clus tering/tutorial_html/AppletKM.htmlhttp://home.dei.polimi.it/matteucc/Clus tering/tutorial_html/AppletKM.html

10. 10 Choosing the number of Clusters K-means method expects number of clusters k to be specified as part of the input How to choose appropriate k? Options –Start with k = 1 and work up to small number Select the result with lowest squared error Warning: Result with each instance in a different cluster on its own would be the best –Create two initial clusters and split each cluster independently When splitting a cluster create two new ‘seeds’ One seed at a point one standard deviation away from the cluster center and the second one standard deviation in the opposite direction Apply k-means to the points in the cluster with the two new seeds

11. 11 Choosing Initial Centroids Randomly chosen centroids may produce poor quality clusters How to choose initial centroids? Options –Take a sample of data, apply hierarchical clustering and extract k cluster centroids Works well – small sample sizes are desired –Take well separated k points Take a sample of data and select the first centroid as the centroid of the sample Each of the subsequent centroid is chosen to be a point farthest from any of the already selected centroids

12. 12 Additional Issues K-means might produce empty clusters Need to choose an alternative centroid Options –Choose the point farthest from the existing centroids –Choose the point from the cluster with the highest squared error Squared error may need to be reduced further –Because K-means may converge to local minimum –Possible to reduce squared error without increasing K Solution –Apply alternate phases of cluster splitting and merging –Splitting – split the cluster with the largest squared error –Merging – redistribute the elements of a cluster to its neighbours Outliers in the data cause problems to k-means Options –Remove outliers –Use K-Medoids instead

13. 13 K-medoid Input: the number of clusters K and the collection of n instances Output: A set of k clusters that minimizes the sum of the dissimilarities of all the instances to their nearest medoids Method: –Arbitrarily choose k instances as the initial medoids –Repeat (Re)assign each remaining instance to the cluster with the nearest medoid Randomly select a non-medoid instance, o r Compute the total cost, S, of swapping O j with O r If S<0 then swap O j with O r to form the new set of k medoids –Until no change

14. 14 K-means Vs K-medoids Both require K to be specified in the input K-medoids is less influenced by outliers in the data K-medoids is computationally more expensive Both methods assign each instance exactly to one cluster –Fuzzy partitioning techniques relax this Partitioning methods generally are good at finding spherical shaped clusters They are suitable for small and medium sized data sets –Extensions are required for working on large data sets