Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting

P. 1250–1257.

Миркин Б. Г., Amorim R.

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.

Ключевые слова: кластеры, clusters, Minkowski metric, метрика Минковского

ПУБЛИКАЦИЯ ПОДГОТОВЛЕНА ПО РЕЗУЛЬТАТАМ ПРОЕКТА: Теоретическое и численное исследование современных математических моделей в социально-экономической, политической и финансовой сферах (2014)

В книге

Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th Birthday

Vol. 92. , Berlin: Springer, 2014.