Learn how to implement the K-Nearest Neighbors (KNN) algorithm from scratch in Python! This tutorial covers the theory, ...

This project used a Kmeans after PCA model to segment retail customers to optimize marketing efforts. When the model repeatedly returned a single cluster, the model was used to prove the customers' ...

Abstract: Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, ...

ABSTRACT: Stock returns exhibit nonlinear dynamics and volatility clustering. It is well known that we cannot forecast the movements of stock prices under the condition that market is efficient. In ...

Rocky high steep slopes are among the most dangerous disaster-causing geological bodies in large-scale engineering projects, like water conservancy and hydropower projects, railway tunnels, and metal ...

Introduction: In unsupervised learning, data clustering is essential. However, many current algorithms have issues like early convergence, inadequate local search capabilities, and trouble processing ...

Abstract: This paper proposes an improved K-means clustering algorithm based on density-weighted Canopy to address the efficiency bottlenecks and clustering accuracy issues commonly encountered by ...

Before the 1975 release of Monty Python and the Holy Grail, the British comedy troupe Monty Python was barely known overseas. People in Britain knew the group, made up of Graham Chapman, John Cleese, ...

ABSTRACT: Accurate sales forecasting is essential in the fast-paced world of business for effective strategic planning and resource allocation. However, traditional forecasting methods often lack ...