K Means Clustering Unsupervised Learning - Udacity Machine Learning Nanodegree Flash Card

• Draw a line connecting two centroids and use the half way line as a division line for two hyperplanes (if two clusters). Results vary greatly.

• Initial positions of centroid can strongly influence result. Different initial positions give completely different results.

• Analogy "Rubber Band"

• Center of the cluster is called a centroid

• Number of centroids at initiation can heavily influence the result. 

• Great for ... PROS:

• Bad for ... CONS ... limitations:
• Hill climbing algorithm.
• Result depends on initiation
• If initiation is close to local optima, may be sticky. Never move away. Ignore global optima. Bad initial centroids exist
• If there are more potential clusters, there are more local optima. Run iterate the algorithm many times to avoid being stuck.