Code and datasets for the Tsetlin Machine
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Updated
Oct 16, 2024 - Cython
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
🍊 📦 Frequent itemsets and association rules mining for Orange 3.
A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.
🔨 Python implementation of Apriori algorithm, new and simple!
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.
Algorithms for Mining Frequent Trees (in Tree Structured Datasets)
fim is a collection of some popular frequent itemset mining algorithms implemented in Go.
Frequent Pattern mining in tree-like sequences for medical data.
Python interface to arules for association rule mining
Market Basket Analysis using Apriori Algorithm on grocery data.
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