Abstract In local Principal Component Analysis (PCA), a distribution is approximated by
multiple units, each representing a local region by a hyper-ellipsoid obtained through PCA …

We present two new clustering algorithms called k-sets and k-swaps for data where each
object is a set. First, we define the mean of the sets in a cluster, and the distance between a …

This paper proposes two algorithms for clustering data, which are variable-sized sets of
elementary items. An example of such data occurs in the analysis of a medical diagnosis …

Background To reduce risk of death in acute ST-segment elevation myocardial infraction
(STEMI), patients must reach a percutaneous coronary intervention (PCI) within 120 min …

Educational big data analysis is facilitated by the significant amount of unstructured data
found in education institutions. Python has various toolkits for both structured and …

K-Means is a “de facto” standard clustering algorithm due to its simplicity and efficiency. K-
Means, though, strongly depends on the initialization of the centroids (seeding method) and …

This paper proposes an algorithm, named HWK-Sets, based on K-Means, suited for
clustering data which are variable-sized sets of elementary items. Clustering sets is difficult …

This paper describes an approach to formal modeling of clustering algorithms based on
medoids using timed automata. The approach permits to assess properties such as the …

K-Means is well-known clustering algorithm very often used for its simplicity and efficiency.
Its properties have been thoroughly investigated. It is emerged that K-Means heavily …