Data clustering is a commonly used data processing technique in many fields, which divides
objects into different clusters in terms of some similarity measure between data points …

The current study seeks to compare 3 clustering algorithms that can be used in gene-based
bioinformatics research to understand disease networks, protein-protein interaction …

Accurate driving cycles are key for effectively evaluating electric vehicle performance. The K-
means algorithm is widely used to construct driving cycles; however, this algorithm is …

The spatial and temporal patterns of rainfall over the Baram River Basin (BR) in Sarawak
(Malaysian Borneo) were characterised through cluster analysis and multivariate statistics …

We present a method enabling the unsupervised discovery of sub-word units (SWUs) and
associated pronunciation lexicons for use in automatic speech recognition (ASR) systems …

High-dimensional single-cell data poses significant challenges in identifying underlying
biological patterns due to the complexity and heterogeneity of cellular states. We propose a …

We investigate the application of two novel lattice-constrained Viterbi training strategies to
the task of improving sub-word unit (SWU) inventories that were discovered using an …

Automatic speech recognition is an increasingly important mode of human-computer
interaction. However, its implementation requires a sub-word unit inventory to be designed …

A voice recognition method is provided. The voice recognition method includes: collecting a
plurality of voice signals; extracting the voiceprint features of each of the voice signals; …

Agglomerative hierarchical clustering (AHC) requires only the similarity between objects to
be known. This is attractive when clustering signals of varying length, such as speech, which …