Traditional clustering methods often fail when faced with complex, non-linear data patterns. This is where density-based clustering comes into play.

Why AI Success Is About Faster, Smarter Data Movement, Not Just Better Models The assumption that AI breakthroughs stem solely from more sophisticated algorithms or powerful GPUs is flawed. AI ...

The SOM algorithm, which incorporated both qualitative and quantitative data, produced the best model, resulting in the identification of three distinct domains. These findings underscore the ...

Existing partition clustering algorithms for handling such data are based on two approaches: conversion of data types, where all data variables are converted to a single data type, and a mixed one, ...

We propose a visual Simultaneous Localization and Mapping (SLAM) algorithm that integrates target detection and clustering techniques in dynamic scenarios to address the vulnerability of traditional ...

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require ...

In data mining, Clustering is the most popular, powerful and commonly used unsupervised learning technique. It is a way of locating similar data objects into clusters based on some similarity.

One of the key issues in clustering is the speci cation of appropriate number of clusters, which is not obvious in many practical situations. In this paper we provide an extension of G-means algorithm ...