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Qborfy AI
Qborfy AI
Jun 26, 2025 · Artificial Intelligence

Unlock Hidden Patterns: A Hands‑On Guide to Unsupervised Learning Techniques

This article explains unsupervised learning by defining its core concepts, comparing clustering, dimensionality reduction, and association techniques, and illustrating each with concrete examples—from restaurant dish grouping and housing decision simplification to convenience‑store product analysis—while offering hands‑on experiments and real‑world case studies such as Amazon, NASA, and 7‑Eleven.

AICase StudiesUnsupervised Learning
0 likes · 5 min read
Unlock Hidden Patterns: A Hands‑On Guide to Unsupervised Learning Techniques
Data Thinking Notes
Data Thinking Notes
Aug 21, 2023 · Product Management

How User Profiling Drives Personalized Marketing and Product Innovation

This article explains the fundamentals, principles, methodologies, and practical applications of user profiling, covering core concepts such as user characteristics, behavior, preferences, needs, and value, the data collection-to-model pipeline, common models like RFM, clustering, association rules, text mining, and how these insights enable personalized recommendation, precise marketing, brand management, service optimization, CRM, market research, and product innovation.

RFM modelSentiment Analysisassociation rules
0 likes · 14 min read
How User Profiling Drives Personalized Marketing and Product Innovation
Python Crawling & Data Mining
Python Crawling & Data Mining
Aug 10, 2020 · Artificial Intelligence

Build Smart Product Recommendations with Python’s Apriori Algorithm

This article explains how intelligent recommendation differs from generic marketing, introduces association‑rule concepts such as support, confidence, and lift, and provides a step‑by‑step Python implementation using the Apriori algorithm to generate and interpret market‑basket recommendations.

Apriori algorithmMarket Basket AnalysisPython
0 likes · 13 min read
Build Smart Product Recommendations with Python’s Apriori Algorithm
Python Crawling & Data Mining
Python Crawling & Data Mining
Mar 17, 2019 · Artificial Intelligence

How Association Rules and Machine Learning Reveal Stock Market Industry Linkages

This report analyzes 2018 AMAC industry index data using association‑rule mining and several machine‑learning models (Apriori, KNN, Bayesian, decision tree, neural network) to uncover sector linkages, predict index and stock movements, compare model performance, and suggest future improvements.

PredictionR languageassociation rules
0 likes · 11 min read
How Association Rules and Machine Learning Reveal Stock Market Industry Linkages
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 8, 2019 · Artificial Intelligence

Unlocking Recommendation Systems: 10 Classic Machine Learning Algorithms Explained

This article surveys ten classic recommendation system algorithms—including collaborative filtering, association rules, Bayesian methods, K‑Nearest Neighbors, decision trees, random forests, matrix factorization, neural networks, word2vec, and logistic regression—detailing their principles, mathematical formulas, and practical implementation steps for real‑world applications.

Recommendation Systemsassociation rulescollaborative filtering
0 likes · 25 min read
Unlocking Recommendation Systems: 10 Classic Machine Learning Algorithms Explained
Big Data and Microservices
Big Data and Microservices
Sep 17, 2018 · Big Data

5 Essential Data Mining Techniques Every Analyst Should Know

This article outlines five widely used data‑mining methods—association rules, classification/tagging, clustering, decision trees, and sequential pattern mining—explaining their principles, real‑world examples, and how they help organizations extract actionable insights from massive datasets.

Big DataDecision TreesSequential Pattern Mining
0 likes · 6 min read
5 Essential Data Mining Techniques Every Analyst Should Know
Architects' Tech Alliance
Architects' Tech Alliance
Nov 24, 2016 · Big Data

Data Mining Overview: Process, Techniques, and Model Evaluation

This article provides a comprehensive introduction to data mining, covering its definition, goal setting, data sampling, exploration, preprocessing, pattern discovery, model building, evaluation methods, and the main analytical techniques such as classification, regression, clustering, association rules, feature and deviation analysis, and web mining.

Model Evaluationassociation rulesclassification
0 likes · 10 min read
Data Mining Overview: Process, Techniques, and Model Evaluation