Clustering is a data mining technique used to analyse data that has variations and the
number of lots. Clustering was process of grouping data into a cluster, so they contained …

Symbols are powerful in branding and marketing to represent tourist attractions. By bridging
semiotics, marketing, and data science in the tourism context, this study uncovers the …

Due to simplicity, K-means has become a widely used clustering method. However, its
clustering result is seriously affected by the initial centers and the allocation strategy makes …

Purpose The COVID-19 crisis has significantly affected entrepreneurial ventures, where
knowledge resources are limited and contextual uncertainty is heightened. This paper aims …

Clustering is one of the main methods for getting insight on the underlying nature and
structure of data. The purpose of clustering is organizing a set of data into clusters, such that …

A modified version of traditional k-means clustering algorithm applying computational
geometry for initialization of cluster centers has been presented in this paper. It is well …

Purpose The paper aims to highlight how an applied learning framework or “community of
practice”(CoP) combined with a traditional theoretical course of study enables the …

Artificial intelligence (AI) is described as the joint set of data entry, able to receive inputs,
interpret and learn from such feedbacks, and display related and flexible independent …

Dramatic increases in climate data underlie a gradual paradigm shift in knowledge
acquisition methods from physically based models to data-based mining approaches. One …

In this paper, an enhanced version of the K-Means algorithm that incorporates a new
convergence criterion is presented. The largest centroid displacement at each iteration was …