The shortest yet efficient Python implementation of the sequential pattern mining algorithm PrefixSpan, closed sequential pattern mining algorithm BIDE, and generator sequential pattern mining algorithm FEAT.
Seq2Pat: Sequence-to-Pattern Generation Library
scikit-mine : pattern mining in Python
Python SPMF Wrapper 🐍 🎁
Pattern Mining for the Classification of Public Procurement Fraud
A Python client CLI and GUI for the SPMF-Server. Programmatically submit pattern mining jobs, poll job status, and fetch results — or use the included graphical interface for interactive exploration of the SPMF data mining library.
This repo is created to perform I/O Request Packet (IRP) driven ransomware analysis where the IRP logs were collected during ransomware execution.
Find data concentration patterns and hotspots. Built for fraud detection and risk analysis.
Find the holes your code already drew.
SIESTA is an application-agnostic, open-source tool designed to build incremental indices from continuously streaming event data.
Explain variable influence in black-box model through pattern mining
Pattern-discovery and rule-management system for anti-spam teams reviewing historical message logs.
An interpretable region-based pattern mining system that incrementally maps LHS feature space to RHS distributions via grid partitioning, clustering, and boundary co-optimization.
Complete association rule mining on Adult Census dataset using FP-Growth — with both mlxtend (efficient) and full from-scratch implementation. Includes data exploration, preprocessing, binarization, and insightful rules on income patterns.
High-performance interpretable ML classifier using High Utility Gain patterns (IEEE Access 2024). C++ accelerated, scikit-learn compatible, with EBM-style explanations, adaptive binning, pattern pruning, and deployment tooling for regulated domains.
This was an assigned computer science project for a summer opportunity. Look at the README.md file for more info!
This project leverages AI techniques including supervised and unsupervised machine learning, pattern mining, and statistical inference to analyze web browsing behavior. It identifies frequent navigation sequences and predicts user demographic profiles using simulated data. Complete with a GUI, logging, and export capabilities.
PatternMining — Implementations of pattern mining algorithms including frequent itemsets, sequential patterns, and Bayesian networks using Python.
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