08-08-2012, 01:43 PM
EFFICIENT WAY OF CLUSTERING GENE DATA USING K-MEANS EXTENSIONS
[attachment=30578]
Introduction
Clustering methods are useful for data reduction, for developing classification schemes and for suggesting or supporting hypotheses about the structure of the data.
An Efficient way of Clustering gene data is by using K-Means Algorithm and its extensions.
Purpose
Our project deals with clustering of gene data for which we have so far used k-means, kernel k-means which are giving good results but with certain drawbacks.
In our project we are trying to statistically analyze clustering performance of clustering algorithms such as K-means, Kernel K-means and CK-means.
Detailed Description
k-means Algorithm
1.Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids.
2.Assign each object to the group that has the closest centroid.
3.When all objects have been assigned, recalculate the positions of the K centroids using euclidian distance formula.
4.Repeat Steps 2 and 3 until the centroids no longer move. This produces a separation of the objects into groups from which the metric to be minimized can be calculated.
Mk-means Algorithm
1.Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids.
2.Assign each object to the group that has the closest centroid.
3.When all objects have been assigned, recalculate the positions of the K centroids using mahalanobis distance formula.
[attachment=30578]
Introduction
Clustering methods are useful for data reduction, for developing classification schemes and for suggesting or supporting hypotheses about the structure of the data.
An Efficient way of Clustering gene data is by using K-Means Algorithm and its extensions.
Purpose
Our project deals with clustering of gene data for which we have so far used k-means, kernel k-means which are giving good results but with certain drawbacks.
In our project we are trying to statistically analyze clustering performance of clustering algorithms such as K-means, Kernel K-means and CK-means.
Detailed Description
k-means Algorithm
1.Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids.
2.Assign each object to the group that has the closest centroid.
3.When all objects have been assigned, recalculate the positions of the K centroids using euclidian distance formula.
4.Repeat Steps 2 and 3 until the centroids no longer move. This produces a separation of the objects into groups from which the metric to be minimized can be calculated.
Mk-means Algorithm
1.Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids.
2.Assign each object to the group that has the closest centroid.
3.When all objects have been assigned, recalculate the positions of the K centroids using mahalanobis distance formula.