HW1: Finding Similar Items: You are to implement the stages of finding textually similar documents based on Jaccard similarity using the shingling, minhashing, and locality-sensitive hashing (LSH) techniques and corresponding algorithms. PySpark code
HW2: Discovery of Frequent Itemsets and Association Rules Implement the Apriori algorithm for finding frequent itemsets with support at least s in a dataset of transactions. Python and PySpark code
HW3: Implementation of "L. De Stefani, A. Epasto, M. Riondato, and E. Upfal: TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size (KDD'16) with Python.
HW4: Implementation of "On Spectral Clustering: Analysis and an algorithm” by Andrew Y. Ng, Michael I. Jordan, Yair Weiss. Matlab and Python code
HW5: Implementation of "F. Rahimian, et al., JA-BE-JA: A Distributed Algorithm for Balanced Graph Partitioning, SASO2013"