Slide 40
Slide 40 text
40/42
Introduction
The K-means algorithm
Discussion about the algorithm
Conclusion
Conclusion
The K-means is the most used clustering algorithm, due to its
inherent simplicity, speed, and empirical success.
However, in its basic form, it has limitations such as the
sensitivity to the initial partition, sensitivity to noise, and the
requierement of predefined number of clusters.
Thus, this algorithm needs to be improved in order to remain
as popular.
J.A Hartigan and M.A Wong Algorithm AS 136 : A K-Means Clustering Algorithm