An implementation of the FP Growth algorithm for support counting
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Updated
Nov 24, 2017 - Java
An implementation of the FP Growth algorithm for support counting
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
Course project for the **Network programming** course in university (FMI at SU)
Genetic Algorithm for frequent itemsets.
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.
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
Closed Frequent Itemset Mining in Data Streams
Package provides java implementation of frequent pattern mining algorithms such as apriori, fp-growth
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