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k-means

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Model Perspective
Model Perspective
Jan 8, 2023 · Artificial Intelligence

Unlock Hidden Patterns: A Deep Dive into Unsupervised Learning Techniques

This article introduces unsupervised learning, covering its motivation, Jensen's inequality, key clustering methods such as EM, k‑means, hierarchical clustering, evaluation metrics, and dimensionality‑reduction techniques like PCA and ICA, providing clear explanations and illustrative diagrams.

ClusteringEM algorithmICA
0 likes · 8 min read
Unlock Hidden Patterns: A Deep Dive into Unsupervised Learning Techniques
vivo Internet Technology
vivo Internet Technology
Jan 4, 2023 · Artificial Intelligence

Root Cause Localization Algorithm and Its Implementation for Service Fault Diagnosis

The article describes a root‑cause localization algorithm implemented in vivo’s monitoring platform that automatically analyzes latency spikes by splitting service timelines, computing variance, clustering results with K‑means, and recursively tracing downstream services, achieving over 85 % accuracy for dependency failures while still requiring human verification and outlining future AI‑driven enhancements.

AIOpsalgorithmfault localization
0 likes · 13 min read
Root Cause Localization Algorithm and Its Implementation for Service Fault Diagnosis
Model Perspective
Model Perspective
Nov 8, 2022 · Artificial Intelligence

Mastering K-Means: How Distance-Based Clustering Works and How to Implement It

This article explains the fundamentals of the K-means clustering algorithm, describing its distance‑based similarity principle, the objective of minimizing squared error, and a step‑by‑step iterative procedure—including random centroid initialization, assignment, centroid recomputation, and convergence criteria.

Clusteringalgorithmk-means
0 likes · 3 min read
Mastering K-Means: How Distance-Based Clustering Works and How to Implement It
Model Perspective
Model Perspective
Aug 18, 2022 · Artificial Intelligence

Master SciPy Clustering: K‑Means and Hierarchical Methods with Python

This guide introduces SciPy's clustering modules, explaining the vector quantization and k‑means algorithm in scipy.cluster.vq, and demonstrates hierarchical clustering with scipy.cluster.hierarchy, accompanied by complete Python code examples and visualizations to help you apply these techniques to real data.

ClusteringPythonSciPy
0 likes · 4 min read
Master SciPy Clustering: K‑Means and Hierarchical Methods with Python
Model Perspective
Model Perspective
Aug 7, 2022 · Artificial Intelligence

Mastering Core ML Evaluation Metrics: From Bias‑Variance to ROC Curves

This article explains essential machine‑learning evaluation concepts—including the bias‑variance trade‑off, Gini impurity versus entropy, precision‑recall curves, ROC and AUC, the elbow method for K‑means, PCA scree plots, linear and logistic regression, SVM geometry, normal‑distribution rules, and Student’s t‑distribution—providing clear visual illustrations for each.

PCAROCbias-variance
0 likes · 7 min read
Mastering Core ML Evaluation Metrics: From Bias‑Variance to ROC Curves
Model Perspective
Model Perspective
Jun 4, 2022 · Artificial Intelligence

Master K-means Clustering: How the Algorithm Finds Compact Groups

K-means is a classic distance‑based clustering algorithm that iteratively partitions data into k compact, well‑separated groups by minimizing the sum of squared errors, using random centroid initialization and heuristic updates until convergence, making it a fundamental tool in AI and data analysis.

Clusteringalgorithmk-means
0 likes · 3 min read
Master K-means Clustering: How the Algorithm Finds Compact Groups
NetEase LeiHuo UX Big Data Technology
NetEase LeiHuo UX Big Data Technology
Feb 24, 2022 · Artificial Intelligence

An Introduction to k-means and Its Variants with Practical R Implementation

This article introduces the k‑means clustering algorithm and its major variants—k‑means++, k‑medoids, k‑medians, k‑modes, and k‑prototype—explaining their characteristics, step‑by‑step procedures, and provides a practical R implementation with code examples for data analysis.

ClusteringData MiningR
0 likes · 11 min read
An Introduction to k-means and Its Variants with Practical R Implementation
Python Programming Learning Circle
Python Programming Learning Circle
Aug 14, 2021 · Artificial Intelligence

Efficient Python Implementation of K-Means Clustering with Performance Comparison

This article introduces a concise Python implementation of the k‑means clustering algorithm, compares its speed with a typical implementation, provides full source code, a data‑generation helper, and demonstrates the results on a synthetic dataset of 10,000 points.

ClusteringNumPyPython
0 likes · 8 min read
Efficient Python Implementation of K-Means Clustering with Performance Comparison
Efficient Ops
Efficient Ops
Jul 26, 2020 · Operations

How LSTM, k‑means, and Probability Density Power Intelligent Anomaly Detection in AIOps

This article explains how WeBank’s intelligent operations team combines LSTM‑based forecasting with Gaussian analysis, k‑means feature clustering, and probability‑density modeling to automatically detect and warn about anomalies in key business metrics, moving beyond traditional threshold‑based monitoring.

AIOpsAnomaly DetectionLSTM
0 likes · 9 min read
How LSTM, k‑means, and Probability Density Power Intelligent Anomaly Detection in AIOps
360 Quality & Efficiency
360 Quality & Efficiency
Nov 2, 2018 · Artificial Intelligence

Extracting Regression from Production Requests Using Clustering Algorithms

This article explains how to apply TF‑IDF weighting and the K‑means clustering algorithm in Python to identify a small set of representative regression cases from hundreds of thousands of production request records, including guidance on selecting the optimal number of clusters.

ClusteringTF-IDFk-means
0 likes · 5 min read
Extracting Regression from Production Requests Using Clustering Algorithms