In practice, a large data set leads to a large K, and storing K may become a problem. One way to deal with this is to perform clustering on the dataset, and populate the kernel with the means of those clusters for speeding up and reducing storage. How can I calculate the kernel using clustering?
Foremost, we must understand what is
It is an unsupervised algorithm, applicable to scenario's where the target class is unknown. So essentially it's a data preprocessing algorithm. Continuing further,
cluster analysis, helps in detecting
patterns in data. Now, to detect patterns in data, one must ensure the
sample consist of
statistically significant variables. To find such significant variables, we need to perform various data preprocessing tasks like
dimensionality reduction detection and treatment. Post, performing these steps, the
noise from the data would be removed and one would be able to reveal the
signal. Otherwise, one can perform the
principal component analysis (pca) to detect the signal. This signal('s), can then aid in determining the true patterns or
clusters in the data.