Exploration on the commonality of hierarchical clustering algorithms

Many clustering algorithms have been devised. The published account of each algorithm emphasizes how it is different from previous algorithms. However, similarities among agglomerative hierarchical algorithms are greater than commonly supposed. For example, several algorithms perform merging by the single link (SLINK, OPTICS) and some algorithms perform merging by the edge cut criterion (CHAMELEON, ROCK). Some algorithms use the square of the original adjacency matrix (OPTICS, ROCK). Our goals are to compose a not very long list of methods used by the various algorithms; to locate each algorithm in this space of methods; and to devise new algorithms that improve upon the previous methods.

[1]  Xiaofeng Zhang,et al.  Concepts and Techniques , 2019, Cognition and Intractability.

[2]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[3]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[4]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[5]  George Karypis,et al.  C HAMELEON : A Hierarchical Clustering Algorithm Using Dynamic Modeling , 1999 .

[6]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

[7]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[8]  Sudipto Guha,et al.  ROCK: a robust clustering algorithm for categorical attributes , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).