@inbook{20195da1512a4358b2b8efe1cc8a4990,
title = "Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting",
abstract = "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.",
author = "{Cordeiro De Amorim}, Renato and Boris Mirkin",
year = "2014",
month = may,
doi = "10.1007/978-1-4939-0742-7_7",
language = "English",
isbn = "978-1-4939-0741-0",
series = "Springer Optimization and Its Applications",
publisher = "Springer Nature ",
pages = "103--117",
booktitle = "Clusters, Orders, and Trees",
address = "Netherlands",
}