Is there any method to find K value in KNN?
Basically too large k may tend to underfit, while smaller k may tend to overfit. You may try to run the classification with different k within the preset range with cross-validation. Observe and find out the k that gives you the least cross validation sum-of-square error.

Another possible metric besides the sum-of-square error is called Silhouette applied to interpretation and validation of clusters of data. The technique provides a succinct graphical representation of how well each object lies within its cluster.