TY - CHAP
T1 - Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting
AU - Cordeiro De Amorim, Renato
AU - Mirkin, Boris
PY - 2014/5
Y1 - 2014/5
N2 - Recently, a three-stage version of K-Means has been introduced, at which not only clusters and their centers, but also feature weights are adjusted to minimize the summary p-th power of the Minkowski p-distance between entities and centroids of their clusters. The value of the Minkowski exponent p appears to be instrumental in the ability of the method to recover clusters hidden in data. This paper advances into the problem of finding the best p for a Minkowski metric-based version of K-Means, in each of the following two settings: semi-supervised and unsupervised. This paper presents experimental evidence that solutions found with the proposed approaches are sufficiently close to the optimum.
AB - Recently, a three-stage version of K-Means has been introduced, at which not only clusters and their centers, but also feature weights are adjusted to minimize the summary p-th power of the Minkowski p-distance between entities and centroids of their clusters. The value of the Minkowski exponent p appears to be instrumental in the ability of the method to recover clusters hidden in data. This paper advances into the problem of finding the best p for a Minkowski metric-based version of K-Means, in each of the following two settings: semi-supervised and unsupervised. This paper presents experimental evidence that solutions found with the proposed approaches are sufficiently close to the optimum.
U2 - 10.1007/978-1-4939-0742-7_7
DO - 10.1007/978-1-4939-0742-7_7
M3 - Chapter (peer-reviewed)
SN - 978-1-4939-0741-0
T3 - Springer Optimization and Its Applications
SP - 103
EP - 117
BT - Clusters, Orders, and Trees
PB - Springer
ER -