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# Kunstmatige Intelligentie / RuG PowerPoint PPT Presentation

KI2 - 7. Clustering Algorithms. Johan Everts. Kunstmatige Intelligentie / RuG. What is Clustering? .

Kunstmatige Intelligentie / RuG

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#### Presentation Transcript

KI2 - 7

Clustering Algorithms

Johan Everts

Kunstmatige Intelligentie / RuG

### What is Clustering?

Find K clusters (or a classification that consists of K clusters) so that the objects of one cluster are similar to each other whereas objects of different clusters are dissimilar. (Bacher 1996)

### The Goals of Clustering

• Determine the intrinsic grouping in a set of unlabeled data.

• What constitutes a good clustering?

• All clustering algorithms will produce clusters,

regardless of whether the data contains them

• There is no golden standard, depends on goal:

• data reduction

• “natural clusters”

• “useful” clusters

• outlier detection

### Hierarchical Clustering

Agglomerative clustering treats each data point as a singleton cluster, and then successively merges clusters until all points have been merged into a single remaining cluster. Divisive clustering works the other way around.

### Agglomerative Clustering

In single-link hierarchical clustering, we merge in each step the two clusters whose two closest members have the smallest distance.

### Agglomerative Clustering

In complete-link hierarchical clustering, we merge in each step the two clusters whose merger has the smallest diameter.

### K-Means

• Step 0: Start with a random partition into K clusters

• Step 1: Generate a new partition by assigning each pattern to its closest cluster center

• Step 2: Compute new cluster centers as the centroids of the clusters.

• Step 3: Steps 1 and 2 are repeated until there is no change in the membership (also cluster centers remain the same)

### Locating the ‘knee’

The knee of a curve is defined as the point of maximum curvature.

### Leader - Follower

• Online

• Specify threshold distance

• Find the closest cluster center

• Distance above threshold ? Create new cluster

• Or else, add instance to cluster

### Leader - Follower

• Find the closest cluster center

• Distance above threshold ? Create new cluster

• Or else, add instance to cluster

### Leader - Follower

• Find the closest cluster center

• Distance above threshold ? Create new cluster

• Or else, add instance to cluster and update cluster center

Distance < Threshold

### Leader - Follower

• Find the closest cluster center

• Distance above threshold ? Create new cluster

• Or else, add instance to cluster and update cluster center

### Leader - Follower

• Find the closest cluster center

• Distance above threshold ? Create new cluster

• Or else, add instance to cluster and update cluster center

Distance > Threshold

### Kohonen SOM’s

The Self-Organizing Map (SOM) is an unsupervised artificial neural network algorithm. It is a compromise between biological modeling and statistical data processing

### Kohonen SOM’s

• Each weight is representative of a certain input.

• Input patterns are shown to all neurons simultaneously.

• Competitive learning: the neuron with the largest response is chosen.

### Kohonen SOM’s

• Initialize weights

• Repeat until convergence

• Select next input pattern

• Find Best Matching Unit

• Update weights of winner and neighbours

• Decrease learning rate & neighbourhood size

Learning rate & neighbourhood size

### Kohonen SOM’s

Distance related learning

### Kohonen SOM’s

• Kohonen SOM Demo (from ai-junkie.com):

mapping a 3D colorspace on a 2D Kohonen map

### Performance Analysis

• K-Means

• Depends a lot on a priori knowledge (K)

• Very Stable

• Depends a lot on a priori knowledge (Threshold)

• Faster but unstable

### Performance Analysis

• Self Organizing Map

• Stability and Convergence Assured

• Principle of self-ordering

• Slow and many iterations needed for convergence

• Computationally intensive

### Conclusion

• No Free Lunch theorema

• Any elevated performance over one class, is exactly paid for in performance over another class

• Ensemble clustering ?

• Use SOM and Basic Leader Follower to identify clusters and then use k-mean clustering to refine.

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