unsupervised learning

Python Programming Learning Circle

Jan 22, 2025 · Artificial Intelligence

A Visual Introduction to Machine Learning: Concepts, Categories, and Techniques

This article provides a clear, illustrated overview of machine learning, explaining its place within artificial intelligence, the main sub‑fields such as supervised and unsupervised learning, classic algorithms, ensemble methods, and practical examples to help beginners grasp core concepts.

Artificial Intelligenceclassificationensemble methods

0 likes · 8 min read

A Visual Introduction to Machine Learning: Concepts, Categories, and Techniques

DataFunSummit

Oct 31, 2024 · Artificial Intelligence

Community Recommendation in Tencent Games: Adaptive K‑Free Community Detection and Constrained Large‑Scale Community Recommendation (ComRec)

This article presents Tencent's research on community recommendation for online games, introducing an adaptive K‑Free community detection algorithm (DAG) to address cold‑start and unknown community count, a constrained large‑scale recommendation method (ComRec), their evaluation metrics, experimental results, and deployment insights.

Graph Neural NetworksRecommendation systemsTencent games

0 likes · 20 min read

Community Recommendation in Tencent Games: Adaptive K‑Free Community Detection and Constrained Large‑Scale Community Recommendation (ComRec)

DataFunTalk

Aug 4, 2024 · Artificial Intelligence

Community Recommendation in Tencent Games: Adaptive K‑Free Community Detection and Constrained Large‑Scale Community Recommendation (ComRec)

This article presents Tencent's research on community recommendation for online games, covering the motivation behind recommending player groups, the challenges of cold‑start and data sparsity, the adaptive K‑Free community detection algorithm (DAG) with joint structural‑semantic learning, the constrained large‑scale ComRec algorithm, extensive offline and online experiments, and practical deployment insights.

Graph Neural NetworksTencent gamescommunity recommendation

0 likes · 20 min read

Python Programming Learning Circle

May 10, 2024 · Artificial Intelligence

Comprehensive Overview of Common Anomaly Detection Methods with Code Examples

This article compiles and explains a variety of common anomaly detection techniques—including distribution‑based, distance‑based, density‑based, clustering, tree‑based, dimensionality‑reduction, classification, and prediction methods—providing algorithm descriptions, workflow steps, advantages, limitations, and ready‑to‑run Python code snippets for each approach.

Anomaly DetectionPythonmachine learning

0 likes · 23 min read

Comprehensive Overview of Common Anomaly Detection Methods with Code Examples

Model Perspective

Mar 8, 2024 · Artificial Intelligence

Master the Three Machine Learning Types and Model Paradigms

This article introduces the three core machine learning categories—supervised, unsupervised, and reinforcement learning—detailing their definitions, typical algorithms, and real‑world applications, and then compares generative and discriminative models, highlighting key examples, characteristics, and use‑case differences.

discriminative modelsgenerative modelsmachine learning

0 likes · 13 min read

Master the Three Machine Learning Types and Model Paradigms

Ctrip Technology

Oct 19, 2023 · Artificial Intelligence

Anomaly Detection and Root Cause Analysis System for Ctrip Train Ticket Business Metrics

This article presents an AI‑driven system that automatically detects anomalies in over 1,000 Ctrip train‑ticket business metrics using six unsupervised algorithms and locates their root causes through a hard‑voting ensemble of four specialized methods, demonstrating practical results and future enhancements.

Anomaly DetectionCtriproot cause analysis

0 likes · 18 min read

Anomaly Detection and Root Cause Analysis System for Ctrip Train Ticket Business Metrics

JD Tech

Sep 12, 2023 · Fundamentals

Community Detection Algorithms: Concepts, Types, and Classic Methods

This article introduces community detection as a fundamental graph algorithm, explains its basic concepts and types, compares it with clustering, discusses evaluation metrics like modularity, and reviews classic methods such as Louvain, node2vec‑based approaches, and the information‑theoretic Infomap algorithm.

InfomapLouvaincommunity-detection

0 likes · 13 min read

Community Detection Algorithms: Concepts, Types, and Classic Methods

Rare Earth Juejin Tech Community

Jul 29, 2023 · Artificial Intelligence

Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples

This article provides a comprehensive overview of machine learning, covering fundamental concepts, key terminology, common algorithms for supervised, unsupervised, and reinforcement learning, model evaluation metrics, loss functions, and practical code examples such as random forest and SVM implementations.

Algorithmsloss functionsmachine learning

0 likes · 35 min read

Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples

Ctrip Technology

May 25, 2023 · Artificial Intelligence

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

This article presents a graph‑neural‑network driven, unsupervised approach that builds heterogeneous user‑feature graphs, learns node weights, constructs user‑user similarity graphs, and applies threshold‑based clustering to identify abnormal registration clusters for fraud detection in Ctrip's business travel platform.

Anomaly Detectionfraud detectiongraph neural network

0 likes · 12 min read

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

Architect

May 24, 2023 · Artificial Intelligence

A Comprehensive Overview of Graph Neural Networks: Models, Techniques, and Applications

Graph Neural Networks (GNNs) have become a research hotspot, and this article provides an intuitive overview of classic GNN models such as GCN, GraphSAGE, GAT, graph auto‑encoders, and DiffPool, discussing their architectures, advantages, limitations, and experimental results across various benchmark datasets.

DiffPoolGATGCN

0 likes · 18 min read

A Comprehensive Overview of Graph Neural Networks: Models, Techniques, and Applications

DataFunSummit

Feb 19, 2023 · Artificial Intelligence

Intelligent Writing Assistant: TexSmart and Effidit Systems, Multi‑Level Unsupervised Text Rewriting, and the New ParaScore Evaluation Metric

This article presents Tencent AI Lab's intelligent writing assistant, detailing the TexSmart text‑understanding platform, the Effidit writing‑assistant features, a multi‑level controllable unsupervised text‑rewriting method, and a novel ParaScore metric that jointly measures semantic similarity and diversity for paraphrase evaluation.

AI writingNLPParaphrase

0 likes · 14 min read

Intelligent Writing Assistant: TexSmart and Effidit Systems, Multi‑Level Unsupervised Text Rewriting, and the New ParaScore Evaluation Metric

DataFunSummit

Feb 6, 2023 · Artificial Intelligence

A Minimalist White‑Box Unsupervised Learning Method Using Sparse Manifold Transform

A recent paper by Prof. Ma Yi and Turing‑Award winner Yann LeCun introduces a simple, interpretable unsupervised learning approach that combines sparse coding, manifold learning, and slow feature analysis, achieving near‑state‑of‑the‑art performance on MNIST, CIFAR‑10, and CIFAR‑100 without data augmentation or extensive hyper‑parameter tuning.

AIdeep learningrepresentation learning

0 likes · 8 min read

A Minimalist White‑Box Unsupervised Learning Method Using Sparse Manifold Transform

DataFunSummit

Feb 4, 2023 · Artificial Intelligence

Overview of Deep Learning Algorithms: Supervised, Unsupervised, and Semi‑Supervised Methods

This article introduces deep learning as a powerful AI technique, explains its core algorithms—including supervised, unsupervised, and semi‑supervised approaches—and provides concrete examples such as CNN, RNN, autoencoders, GAN, self‑supervised and transfer learning, illustrated with visual demos.

AIGANdeep learning

0 likes · 6 min read

Overview of Deep Learning Algorithms: Supervised, Unsupervised, and Semi‑Supervised Methods

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

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

Nov 5, 2022 · Artificial Intelligence

Explore the Most Popular Machine Learning Algorithms and How They Work

This comprehensive guide walks you through the most popular machine learning algorithms, explaining how they are classified by learning style and problem type, and highlighting key examples from supervised, unsupervised, deep learning, ensemble, and many other algorithm families.

Algorithmsdeep learningmachine learning

0 likes · 11 min read

Explore the Most Popular Machine Learning Algorithms and How They Work

Model Perspective

Oct 26, 2022 · Artificial Intelligence

Master Machine Learning Algorithms: Types, Python Code & Real-World Examples

This article categorizes machine learning algorithms into supervised, unsupervised, and reinforcement learning, then details ten common algorithms—including linear regression, logistic regression, decision trees, SVM, Naive Bayes, K‑NN, K‑means, random forest, and dimensionality reduction—accompanied by clear Python code examples and illustrative diagrams.

AlgorithmsPythonmachine learning

0 likes · 14 min read

Master Machine Learning Algorithms: Types, Python Code & Real-World Examples

HomeTech

Sep 8, 2022 · Artificial Intelligence

Concept Tag Mining for Recommendation Systems: Methods, Challenges, and Solutions

This article presents a comprehensive overview of concept tag mining for recommendation systems, describing unsupervised pattern‑matching, semi‑supervised AutoPhase, and supervised NER approaches, analyzing their advantages and drawbacks, and offering practical solutions to tag duplication and quality issues.

NERNLPRecommendation systems

0 likes · 11 min read

Concept Tag Mining for Recommendation Systems: Methods, Challenges, and Solutions

Model Perspective

Aug 5, 2022 · Artificial Intelligence

What Are the Essential Steps and Types of Machine Learning?

Machine learning involves five core steps—from data collection and preparation to model training, evaluation, and improvement—while encompassing supervised, unsupervised, and reinforcement learning methods, each with distinct algorithms and real-world applications across finance, healthcare, and retail.

applicationsmachine learningreinforcement learning

0 likes · 7 min read

What Are the Essential Steps and Types of Machine Learning?

Model Perspective

Aug 3, 2022 · Artificial Intelligence

Explore the Most Popular Machine Learning Algorithms: A Comprehensive Guide

This article provides a thorough overview of the most widely used machine learning algorithms, classifying them by learning style and problem type, and highlighting popular methods such as supervised, unsupervised, semi‑supervised, regression, instance‑based, regularization, decision‑tree, Bayesian, clustering, association rule, neural network, deep learning, dimensionality‑reduction, and ensemble techniques.

Algorithmsclassificationdeep learning

0 likes · 10 min read

Explore the Most Popular Machine Learning Algorithms: A Comprehensive Guide