Directing at the problem of inaccurate expression of tacit knowledge and low heuristic of
retrieval results in the design inspiration drawing process of designers based on traditional …

Interval-valued functional clustering is a novel approach for functional data analysis where
each observation is represented by an interval. Existing interval-valued functional clustering …

The k‐means algorithm is sensitive to the outliers. In this paper, we propose a robust two‐
stage k‐means clustering algorithm based on the observation point mechanism, which can …

K-Means algorithm is an unsupervised learning algorithm, which is widely used in machine
learning and other fields. It has the advantages of simple thought, good effect and easy …

Emergency event monitoring is a hot topic in wireless sensor networks (WSNs). Benefiting
from the progress of Micro-Electro-Mechanical System (MEMS) technology, it is possible to …

Despite the popularity of k-means clustering, feature scaling before applying it can be an
essential yet often neglected step. In this study, feature scaling via five methods: Z-score …

The traditional K-means algorithm is very sensitive to the selection of clustering centers and
the calculation of distances, so the algorithm easily converges to a locally optimal solution …

Pengelompokan K-Means bertujuan untuk mengumpulkan satu set titik pusat cluster yang
optimal melalui iterasi yang berurutan. Fakta bahwa semakin optimal posisi dari titik pusat …

According to the characteristics of road traffic accident data, two improved data mining
methods are used to analyse the risk of accidents: nine accident-related factors are selected …