A tool for mining HTTP request frequent patterns

My minor research in 10 months | Data Stream Analysis with Storm Apache | Frequent Pattern Mining on Item, Itemset | Sep 2014 - July 2015

Works in my Master thesis in 2010 related the Frequent Sequence Mining topic. I implemented the PRISM algorithm and extended the PRISM into the distributed database scheme. The PRISM algorithm proposed in K. Gouda, M. Hassaan and M. J. Zaki, "Prism: A Primal-Encoding Approach for Frequent Sequence Mining," Seventh IEEE International Conference o…

3 notebooks covering Classification, Clustering Analysis and Frequent Pattern Mining in the scope of Data Mining lectures in Marmara University.

"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.

Mining Interesting Patterns from Uncertain Databases

Data mining on university of twente website

[CSE 4255] Introduction to Data Mining and Warehousing Lab

Original ECLAT algorithm for frequent itemset mining

Association-Rules-Data-Mining-Books. Apriori Algorithm, Association rules with 10% Support and 70% confidence, Association rules with 20% Support and 60% confidence, Association rules with 5% Support and 80% confidence, visualization of obtained rule.

Apriori Algorithm Association rules with 10% Support and 70% confidence Association rules with 5% Support and 90% confidence Lift Ratio > 1 is a good influential rule in selecting the associated transactions visualization of obtained rule

C++ implementation of FPtree, a part of FPGrowth algorithm (Frequent Pattern Mining)

Python interface to arules for association rule mining

The minDNF method samples minimal boolean expressions in DNF.

Improving frequent pattern tree algorithm by introducing extra dimensionality to the items in itemset.

Apriori algorithm implementation (Introduction to Data Mining / Problem set 1)

EAFIM: efficient apriori-based frequent itemset mining algorithmon Spark for big transactional data