Deepak Turaga 1, Michalis Vlachos 2, Olivier Verscheure 1 1 IBM T.J. Watson Research Center, NY, USA...

Deepak Turaga1, Michalis Vlachos2, Olivier Verscheure1

1IBM T.J. Watson Research Center, NY, USA2IBM Zürich Research Laboratory, Switzerland

On K-Means Cluster Preservation using Quantization Schemes

overview – what we want to do…• Examine under what conditions compression methodologies

retain the clustering outcome• We focus on the K-Means algorithm

k-Means

cluster 1 cluster 2 cluster 3 cluster 1 cluster 2 cluster 3

k-Means

identical clustering

results

original data quantized data

why we want to do that…

• Reduced Storage– The quantized data will take up less space

why we want to do that…

• Reduced Storage– The quantized data will take up less space

• Faster execution– Since the data can be represented in a more

compact form the cluster algorithm will require less runtime

why we want to do that…

• Reduced Storage– The quantized data will take up less space

• Faster execution– Since the data can be represented in a more compact

form the cluster algorithm will take less runtime

• Anonymization/Privacy Preservation– The original values are not disclosed

why we want to do that…

• Reduced Storage– The quantized data will take up less space

• Faster execution– Since the data can be represented in a more compact form the

cluster algorithm will take less runtime

• Anonymization/Privacy Preservation– The original values are not disclosed

• Authentication– encode some message with the quantization

We will achieve the above and still guarantee same results

other cluster preservation techniques

• We do not transform into another space• Space requirements same – no data simplification• Shape preservation

[Oliveira04] S. R. M. Oliveira and O. R. Zaane. Privacy Preservation When Sharing Data For Clustering, 2004[Parameswaran05] R. Parameswaran and D. Blough. A Robust Data Obfuscation Approach for Privacy Preservation of Clustered Data, 2005

original

quantized

K-Means Algorithm:

1. Initialize k clusters (k specified by user) randomly.

2. Repeat until convergence

1. Assign each object to the nearest cluster center.

2. Re-estimate cluster centers.

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k-means overview

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k-means example

k-means applications/usage

• Fast pre-clustering

k-means applications/usage

• Fast pre-clustering

• Real-time clustering (eg image, video effects)– Color/Image segmentation

k-means objective function

• Objective: Mininize sum of intra-class variance

Cluster centroid

After some algebraic manipulations

clusters

Dimensions/Time instances 2nd moment 1st moment

k-means objective function

So we can preserve the k-Means outcome if:

clusters

Dimensions/Time instances 2nd moment 1st moment

• We maintain the cluster assignment• We preserve the 1st and 2nd moment of the cluster objects

moment preserving quantization

• 1st moment: average• 2nd (central) moment :

variance• 3rd moment: skewness• 4th moment: kyrtosis

In order to preserve the first and second moment we will use the following quantizer:

g

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Everything below the mean valueis ‘snapped’ here

Everything above the mean valueis ‘snapped’ here

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g

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Everything above the mean valueis ‘snapped’ here

Everything below the mean valueis ‘snapped’ here

= 0.2049g

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= -1.4795

-2.4240-0.22380.0581

-0.4246-0.2029-1.5131-1.1264-0.81500.3666

-0.58611.53740.1401

-1.8628-0.4542-0.65210.1033

-0.2206-0.2790-0.7337-0.0645

original -1.4795 0.2049 0.2049 0.2049 0.2049 -1.4795 -1.4795 -1.4795 0.2049 -1.4795 0.2049 0.2049 -1.4795 0.2049 -1.4795 0.2049 0.2049 0.2049 -1.4795 0.2049

quantized

-0.4689average =

-0.4689average =

These are the points for one dimension and for one cluster of objects.

Process is repeated for all dimensions and for all clustersWe have one quantizer per class

Dimension d (or time instance d)

our quantization

• One quantizer per class• The quantized data are binary

our quantization

• The fact the we have 1 quantizer per class suggests that we need to run k-Means once before we quantize

• This is not a shortcoming of the technique as we need to know the cluster boundaries so that we know how much we can simplify the data.

why quantization works?

• Why does the clustering remain same before and after quantization?

Centers do not change (averages remain same)

why quantization works?

• Why does the clustering remain same before and after quantization?

Centers do not change (averages remain same)

Cluster assignment does not change because clusters ‘shrink’due to quantization

will it always work?

• The results will be the same for datasets with well-formed clusters

• Discrepancy of results means that clusters were not that dense

recap

• Use moment preserving quantization to preserve objective function

• Due to cluster shrinkage, cluster assignments will not change

• Identical results for optimal k-Means• One quantizer per class• 1-bit quantizer per dimension

clusters

Dimensions 2nd moment 1st moment

example: shape preservation

example: shape preservation

example: shape preservation

[Bagnall06] A. J. Bagnall, C. A. Ratanamahatana, E. J. Keogh, S. Lonardi, and G. J. Janacek. A Bit Level Representation for Time Series Data Mining with Shape Based Similarity. In Data Min. Knowl. Discov. 13(1), pages 11–40, 2006.

example: cluster preservation

• 3 years Nasdaq stock ticker data• We cluster into k=8 clusters

Confusion Matrix

8

3% mislabeled data after the moment preserving quantization

With Binary Clipping: 80% mislabeled

Clu

ster

cen

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quantization levels indicate cluster spread

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example: label preservation

• 2 datasets– Contours of fish – Contours of leaves

• Clustering and then k-NN voting

Acer platanoides Salix fragilis Tilia Quercus robur

For rotation invariance we use a rotation invariant features

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example: label preservation

• Very low mislabeling error for MPQ

• High error rate for Binary Clipping

other nice characteristics

• Low sensitivity to initial centers– Mismatch when starting from different centers is

around 7%

other nice characteristics

• Low sensitivity to initial centers– Mismatch when starting from different centers is

around 7%

• Neighborhood preservation – even though we are not optimizing directly that…– Good results because we are preserving the ‘shape’

of the object

AB

size reduction by a factor of 3when using the quantized scheme

• Compression reduces for increasing K

summary

• 1-bit quantizer per dimension sufficient to preserve kMeans ‘as well as possible’

• Theoretically the results will be identical (under conditions)

• Good ‘shape’ preservation

Future work:• Multi-bit quantization• Multi-dimension quantization

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