  K-Means clustering algorithm is one of the most commonly used clustering
algorithms because of its simplicity and efficiency. K-Means clustering
algorithm based on Euclidean distance only pays attention to the linear
distance between samples, but ignores the overall distribution structure of the
dataset (i.e. the fluid structure of dataset). Since it is difficult to
describe the internal structure of two data points by Euclidean distance in
high-dimensional data space, we propose a new distance measurement, namely,
view-distance, and apply it to the K-Means algorithm. On the classical manifold
learning datasets, S-curve and Swiss roll datasets, not only this new distance
can cluster the data according to the structure of the data itself, but also
the boundaries between categories are neat dividing lines. Moreover, we also
tested the classification accuracy and clustering effect of the K-Means
algorithm based on view-distance on some real-world datasets. The experimental
results show that, on most datasets, the K-Means algorithm based on
view-distance has a certain degree of improvement in classification accuracy
and clustering effect.
