Abstract: With the continuous development of data storage, analysis, processing and other technologies, people are eager to visualize the mining process of complex data, and data mining visualization ...

However, traditional unsupervised clustering algorithms (such as K-means, DBSCAN, hierarchical clustering, etc ... this method not only enhances the speed of data mining and pattern recognition but ...

COLLEGE STATION, Texas (KBTX) - With a city council vote approaching on Thursday, Priority Power CEO Brandon Schwertner is clearing the air about what he calls misconceptions about a proposed data ...

This project applies hierarchical clustering to group car models by attributes like horsepower, price, and fuel efficiency. It involves data preprocessing, cluster analysis, and visualization to ...

Introduction: The relationship between physical activity and anxiety among students has been extensively studied, with research highlighting the protective effects of physical activity on mental ...

BACKGROUND Leprosy, a neglected tropical disease caused by Mycobacterium leprae, presents significant public health challenges in Brazil due to its slow progression, dermato-neurological ...

ABSTRACT: This paper presents a new algorithm for solving unit commitment (UC) problems using a binary-real coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Multiclass data sets and large-scale studies are increasingly common in omics sciences ...

HBCC is a clustering algorithm is inspired by the work of Peng et al. [4]. Their CDC algorithm detects regions that separate clusters by quantifying how spread out points' neighbors are in feature ...

1 Facultad de Ingeniería, Universidad Andres Bello, Santiago, Chile. 2 Department of Mining Engineering, Universidad de Chile, Santiago, Chile. 3 Advanced Mining Technology Center, Universidad de ...