Some experiments about Machine Learning
Implementation of the Apriori and Eclat algorithms, two of the best-known basic algorithms for mining frequent item sets in a set of transactions, implementation in Python.
Hadoop MapReduce implementation of Market Basket Analysis for Frequent Item-set and Association Rule mining using Apriori algorithm.
FPGrowth Algorithm implementation in TypeScript / JavaScript.
Analytics and Systems of Big Data
"Frequent Mining Algorithms" is a Python library that includes frequent mining algorithms. This library contains popular algorithms used to discover frequent items and patterns in datasets. Frequent mining is widely used in various applications to uncover significant insights, such as market basket analysis, network traffic analysis, etc.
fim is a collection of some popular frequent itemset mining algorithms implemented in Go.
Python project for Market Basket Analysis. Generates synthetic retail transactions, mines frequent itemsets using Apriori & FP-Growth, derives association rules, and outputs CSVs + visualizations. Portfolio-ready example demonstrating data science methods for uncovering product co-purchase patterns.
Apriori Algorithm implementation in TypeScript / JavaScript.
Implementation of Apriori and Eclat Algorithms (With CPU & GPU)
A library of scalable frequent itemset mining algorithms based on Spark
Market Basket Analysis using Apriori Algorithm on grocery data.
This is an implementation of Apriori algorithm for frequent itemset generation and association rule generation. The GUI is made using JAVA FX or Cmd_Line version can be used
MS-Apriori is used for frequent item set mining and association rule learning over transactional data.
Data Mining course projects
Data Mining course projects
⚡️ 📊 A fast multi-threaded implementation of the PaNDa+ algorithm for mining Top-K Binary patterns in transactional data.
A Parallel Implementation of The Apriori Algorithm on AiMOS Supercomputer Using CUDA and MPI
Mining recipe data from Allrecipes using collaborative filtering and frequent itemset mining.
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