1. A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets
Ashok Sharma, Robert Podolsky, Jieping Zhao and Richard A. McIndoe BIOINFORMATICS, 2009

2. Outline
Introduction
Methods
Results
Discussion

3. Introduction Three common clustering algorithms:
Hierarchical Clustering
K-means
Self-organizing maps(SOM)

4. Introduction
The size of the dataset becomes an issue in cluster analysis with technological advances.
It needs much memory and CPU time to perform cluster analysis for large datasets.

5. Introduction A two stage algorithm would be proposed:
The first stage: reduce the data complexity.
The second stage: cluster the resulting partitions from the first stage.
Two stage algorithm: HPCluster
Gene expression profiles
Reduce the data complexity
Cluster the resulting partitions
Clustering results

6. Methods Algorithm: HPCluster
Estimation of the maximum diameter of a CF
Initial centroids calculation schemes

7. Algorithm: HPCluster HPCluster works in two stages:
Reduction of the dataset using cluster features (CFs).
conventional K-means clustering on the CFs.
How to reduce the scale of dataset?
Two stage algorithm: HPCluster
Gene expression profiles
Reduce the data complexity
Cluster the resulting partitions
Clustering results
cluster features
K-means

8. How to reduce the scale of dataset?
Cluster features(CFs): A CF is a group of closely related data points and is defined as:
: Number of data points in the CF.
: Linear sum of the vector data points.
: Sum of the squares of the data points.

9. How to reduce the scale of dataset?Q
where is the d-dimensional data point vector.

10. How to reduce the scale of dataset?
The centroid, radius and diameter of a CF can be calculated:

11. HPCluster: The first stage

12. Estimation of the maximum diameter of a CF
The degree of data reduction depends upon the maximum allowable size of the CFs.
How to estimating the initial ?

13. Estimation of the maximum diameter of a CF
We use rule to assess the similarity of the data between nodes of a cluster.
Based on the observation, ∼90% of the agglomerations in a hierarchical cluster of non-uniform data will initially have very small distances as compared with the maximum closest pair distances (the last 10% of agglomerations).

14. Estimation of the maximum diameter of a CF
We randomly choose 10% of the genes and perform hierarchical clustering.
The inflection point of the distance plot indicates a sharp increase from the smallest distance pairs to the largest distance pairs (the last 10% of agglomerations).

15. HPCluster: The second stage

16. Initial centroids calculation schemes
Because of the usage of K-means algorithm, we need to set the initial centroids:
Dense regions from data (DEN)
Random initial assignments (RIA)

17. Dense regions from data (DEN)
After completing the first stage, we sort the CFs based on the value of in each CF and use the top k number of clusters as the initial centroids.
Cluster features (CFs)
top k
Sorting based on the value of (Number of data points)

18. Random initial assignments (RIA)
The initial centroids are calculated based on a random assignment of each gene to a cluster followed by a calculation of the mean for the random clusters.
If k = 3, we assume there are 3 clusters:
genes C1
the mean of the clusters
The 3 initial centroids
random assignment C2 C3

19. Results Experimental setting Significantly reduced time to completion
Evaluation of cluster quality
Effect of incorrect cluster number

20. Experimental setting
The machine: Dell Poweredge 2650 machine with Dual GHz/512K Cache Xeon Processors and 8.0 GB DDR 266MHz RAM.
K-means and SOM algorithm are performed by using R.
Eisen Cluster software version 2.0 and 3.0 are compared.

21. Experimental setting
HPCluster was written using the .NET Framework v1.1 and C#.

22. Experimental setting Data standardization for centering:
: the expression value of the i-th gene across the j-th array
: the mean expression of the i-th gene
: the standard deviation of the i-th gene.

23. Significantly reduced time to completion
We clustered both the simulated and real microarray datasets for both centered and un-centered data and recorded the time to completion.
Each dataset had 100 arrays, varying numbers of genes (10 000, and ), varying numbers of clusters (4, 10 and 20) and run 12 times each.

24. Significantly reduced time to completion
The comparison result for centered data:

25. Significantly reduced time to completion
The comparison result for un-centered data:

26. Evaluation of cluster quality
To evaluate the quality of the cluster assignments produced by HPCluster, we use the adjusted Rand index (ARI) for experiment.
Accuracy was evaluated on the simulated datasets using the ARI calculated between the output cluster partitions and the true partitions.

27. Evaluation of cluster quality
The result:
The average ARI results for the accuracy of the gene assignments for genes and 20 true clusters for both the centered and un-centered data after running each program 12 times.

28. Evaluation of cluster quality
We examined cluster stability for analyses involving the experimental data.
The stability plots for both the centered and un-centered gene experimental dataset using k =10 clusters.

29. Effect of incorrect cluster number
Determining the appropriate number of clusters to use can be difficult and has an impact on the accuracy and usefulness of the resulting cluster analysis.
To determine the effect of using an incorrect number of clusters, we ran HPCluster under varying numbers of clusters.

30. Effect of incorrect cluster number
The result for centered data:

31. Effect of incorrect cluster number
The result for un-centered data:

32. Discussion
Partitioning methods such as k-means or SOM are more suitable to cluster the larger datasets.
What factors can affect the speed and accuracy of clustering methods?
The size and complexity of the data.
How the data is processed before analysis.
In this study, we investigated the effect of the number of genes, clusters and arrays on the time to completion as well as the effect of preprocessing the data.

33. Discussion
Depending on the design of the microarray experiment, it may be advantageous to center your data before beginning a cluster analysis.
All the programs we tested were sensitive to whether or not the data was centered prior to cluster analysis.
From the result:
When the data is centered: the DEN initialization.
When the data is un-centered: the RIA initialization.

34. Discussion
The GEO at NCBI currently has 706 Homo sapiens datasets and over gene expression measurements.
As the number of publically available microarray experiments increases, the ability to analyze extremely large datasets across multiple experiments becomes critical.

35. Gene Expression Omnibus(GEO)
The website: