clustering - Kmeans, Agglomerative, and DBSCAN about python-for-data-and-media-communication-gitbook HOT 3 OPEN

Sounds like a case where:

• Data points are too close to each other, relatively speaking, compared with the distance between centroid.
• One centroid captures all the data points at a glance and updates itself to the centre of those points; while all other centroid has no data to update themselves.

Without looking at the exact data, you can try the following methods:

1. Normalise all the records, in case the distance is too small in original space.
2. Manually set K-means centroid initial values. If you intend to have K clusters, randomly pick K data points to be the initial centroids.

Which algorithm is better is hard to tell without looking at the data. We can focus on making K-means work first.

from python-for-data-and-media-communication-gitbook.

In sklearn.cluster.KMeans if set ( init=’random’), seems the centroid will be randomly picked. And if random to choose centroids and run for many times but obey the same result, I guess it rules out the possibility that the distance between data points is smaller than the distance between centroid, because it's already random.

Looks like there left only one situtation that there is no distance at all of are the data points.

from python-for-data-and-media-communication-gitbook.

If the dataset has infinite many data points, seems under some distance constrictions the data shape like Paraboloid may result in just only one non-empty cluster. But for the dataset with limited data points, it's unlikely to have the result.

from python-for-data-and-media-communication-gitbook.