Selected tasks (2022)

During the course everyone will implement one clustering algorithm which will be constructed stepwise during the course. At minimum, this will be some k-means variant but if some students want to take some more challenging algorithm then this page will be used to coordinate the efforts.

Algorithm:	C	C++	C#	Java	JS	R	Matlab	PHP	Go	Ruby
Genetic Algorithm (GA)
Density-based (Dens)
Density Peaks (DenP)
Mean shift (MS)
Affinity propagation (AP)
Stochastic relaxation (SR)
Fast k-means (F-KM)
Agglomerative clustering (AC)
Centroid index (CI)	PF

References

GA: P. Fränti, "Genetic algorithm with deterministic crossover for vector quantization", Pattern Recognition Letters, 21 (1), 61-68, 2000
DenP: A. Rodriquez and A. Laio, Clustering by fast search and find of density peaks, Science, 344 (6191), 1492-1496, 2014.
MS: Y. Cheng, "Mean shift, mode seeking, and clustering", IEEE Trans. on Pattern analysis and Machine Intelligence, 17 (8), 790-799, 1995.
AP: B.J. Frey, D. Dueck, "Clustering by passing messages between data points", Science, 315, 972-976, 2007.
SR: K. Zeger and A. Gersho, "Stochastic Relaxation Algorithm for Improved Vector Quantiser Design", Electronics Letters, 25 (14), 896-898, July 1989.
F-KM: T. Kaukoranta, P. Fränti and O. Nevalainen, "A fast exact GLA based on code vector activity detection", IEEE Trans. on Image Processing, 9 (8), 1337-1342, August 2000
AC: P. Fränti, T. Kaukoranta, D.-F. Shen and K.-S. Chang, "Fast and memory efficient implementation of the exact PNN", IEEE Trans. on Image Processing, 9 (5), 773-777, May 2000.

Summary

Text summary: here
Pseudo codes: RS AC GA

RS pseudo code includes only standard k-means for simplicity but GA pseudo code includes the Fast k-means.