Why so many clustering algorithms: a position paper
V Estivill-Castro - ACM SIGKDD explorations newsletter, 2002 - dl.acm.org
ACM SIGKDD explorations newsletter, 2002•dl.acm.org
We argue that there are many clustering algorithms, because the notion of" cluster" cannot
be precisely defined. Clustering is in the eye of the beholder, and as such, researchers have
proposed many induction principles and models whose corresponding optimization problem
can only be approximately solved by an even larger number of algorithms. Therefore,
comparing clustering algorithms, must take into account a careful understanding of the
inductive principles involved.
be precisely defined. Clustering is in the eye of the beholder, and as such, researchers have
proposed many induction principles and models whose corresponding optimization problem
can only be approximately solved by an even larger number of algorithms. Therefore,
comparing clustering algorithms, must take into account a careful understanding of the
inductive principles involved.
We argue that there are many clustering algorithms, because the notion of "cluster" cannot be precisely defined. Clustering is in the eye of the beholder, and as such, researchers have proposed many induction principles and models whose corresponding optimization problem can only be approximately solved by an even larger number of algorithms. Therefore, comparing clustering algorithms, must take into account a careful understanding of the inductive principles involved.