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

Analysing purchasing patterns of items from an online retail company.

Code and datasets for the Tsetlin Machine

Transform time series to a pattern-based embedding

🍊 📦 Frequent itemsets and association rules mining for Orange 3.

🔨 Python implementation of Apriori algorithm, new and simple!

Assignments done under course COL761-Data Mining

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

Data Mining Course - Fall 2024

A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E).

Python interface to arules for association rule mining

The Tokenizer is a versatile text processing library written in Visual Basic (VB.NET). It provides functionalities for tokenizing text into words, sentences, characters, and n-grams. The library is designed to be flexible, customizable, and easy to integrate into your VB.NET projects.

"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.

💳 Explore Decision Tree, Naive Bayesian and Classification using Frequent Patterns in detecting credit card fraudulent transactions

this is a backend application using springboot to implement the apriori method for association rules generation

An association rule learning-based product recommendation system is desired to be created using the dataset containing users who received services and the categories of services they received.

Apriori algorithm is used in mining frequent item sets and relevant association rules, describing how items are related to one another.

Write a code to implement FP-growth (Frequent Pattern Mining) algorithm and output frequent itemset with support >=2500

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