Code and datasets for the Tsetlin Machine

PAMI is a Python library containing 100+ algorithms to discover useful patterns in various databases across multiple computing platforms. (Active)

Implements the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, Weighted Tsetlin Machine, and Embedding Tsetlin Machine, with support for continuous features, multigranularity, clause indexing, and literal budget

🔨 Python implementation of Apriori algorithm, new and simple!

A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.

🍊 :package: Frequent itemsets and association rules mining for Orange 3.

Tutorial on the Convolutional Tsetlin Machine

Using the Tsetlin Machine to learn human-interpretable rules for high-accuracy text categorization with medical applications

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.

Multi-threaded implementation of the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features and multigranularity.

A handy Python wrapper of the famous VMSP algorithm for mining maximal sequential patterns.

Implementation of FPTree-Growth and Apriori-Algorithm for finding frequent patterns in Transactional Database.

gSpan, an efficient algorithm for mining frequent subgraphs

cSPADE: mining frequent sequence patterns with constraints (extension of SPADE)

"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.

Python interface to arules for association rule mining

fim is a collection of some popular frequent itemset mining algorithms implemented in Go.

Algorithms for Mining Frequent Trees (in Tree Structured Datasets)

Frequent Pattern mining in tree-like sequences for medical data.

Frequent Pattern for DBLP

Market Basket Analysis using Apriori Algorithm on grocery data.

in Data-Mining Field, when we try to create recommendation system depends on transactions, we need to analyze it to extract frequent patterns that it's essential to build association rules.

Directed Multigraph Miner (DMGM)

SLEUTH Algorithm for mining frequent embedded/induced unordered/ordered trees

FPGrowth(Frequent Pattern Mining) implementation in C# .NET

Frequent patten mining using apriori algorithm with hast tree for Amazon review data around 6M users.

Algorithms for Frequent-pattern Mining

Pattern Discovery implemented in C#

A property recommender for Knowledge Graph authoring, first presented at ESWC 2020

Distributed frequent sequence mining with declarative subsequence constraints