This project is an Association Rule Mining implementation combining the Apriori algorithm with MapReduce that was implemented in the Masters of Advanced Analytics at Nova IMS
-
Updated
May 9, 2017 - Java
This project is an Association Rule Mining implementation combining the Apriori algorithm with MapReduce that was implemented in the Masters of Advanced Analytics at Nova IMS
Closed Frequent Itemset Mining in Data Streams
An implementation of the FP Growth algorithm for support counting
Understanding Big Data Analytics by using Map Reduce for performing various tasks like Blooms Filter, Frequent Itemset, KMeans, Matrix Multiplication, Finding Maximum Temperature, Finding Word Count, and Analyzing Electricity Consumption
Package provides java implementation of frequent pattern mining algorithms such as apriori, fp-growth
Coursework for CS550 : Massive Data Mining. Topics covered include Map-Reduce, Association Rules, Frequent Itemsets, Locality-Sensitive Hashing (LSH), Singular Value Decomposition (SVD), Page Rank, k-means, Modularity, Spectral Clustering, Clique-based communities, Clustering Data Streams.
Course project for the **Network programming** course in university (FMI at SU)
Add a description, image, and links to the frequent-itemsets topic page so that developers can more easily learn about it.
To associate your repository with the frequent-itemsets topic, visit your repo's landing page and select "manage topics."