INF 553: Foundations and Applications of Data Mining

Fall 2018

Lecture Slides

This page contains additional material for the USC’s INF 553/CSCI 541 – Foundations and Applications of Data Mining (Fall 2018) course. A complete list of materials, assignments, and course information is available on blackboard (https://blackboard.usc.edu).

•    L01 – Foundations and Applications of Data Mining (Aug 24)
•    L02 – Introduction to MapReduce (cont.) (Aug 31)
•    L09 – Clustering (Oct 19)
•    L10 – Mining Social Network Graphs (Oct 26)
•    L11 – Mining Social Network Graphs (Overlapping Communities) (Nov 2)
•    L12 – Web Advertising (Nov 9)
•    L13 – Mining Data Streams (Nov 16)

Example Programs

Example of Python-based programs using Jupyter Notebooks for supporting the lecture slides

• MapReduce
•    MapReduce-PySpark-Examples.ipynb.zip
•    MapReduce-PySpark-Examples-2.ipynb.zip
• Clustering
•    Hierarchical-Clustering-Example.zip
•    K-Means-Clustering-Example.zip
•    Clustering-Comparison.zip
• Mining Social Network Graphs
•    mining-social-network-graphs-part1.zip
•    mining-social-network-graphs-part2.zip

Project Reports

   Agenda Endorsement System

A. Premkumar, P. Muralidharan, R. Malla, S. Ravishankar

   A Comparison of Graph-Based Recommendation System with Classical Recommendation Methods

A. Rawat, A. Rajendran, R. Chugh, Z. Xu

   Recommendations for Restaurant Improvement using Random Walk Based Opinion Mining

E. Rita, S. Venkatesh, S. Raviillu

   Las Vegas Business Closure Prediction Model

X. Chen, Y. Fei, Y. Wang, F. Zhang

   Yelp Recommendation System via User’s Personality and Sentiment Analysis in Reviews

R. Shao, D. Huang, K. Wang, X. Tao

   Predicting Restaurant Health using Yelp data and Government Inspections

A. Anand, D. Purohit, R. Kaur, R. Goomer

   Recommending the Target Audience to Businesses

D. Ramnani, P. Vaghani, S. Parikh, V. Thakkar, K. Mehta

   Friend Suggestions to Yelp Users based on Similar Interest

M. Shivarudrappa, S. Srinivasa, S. G. Sheshadri, V. P. Vijayakumar

   Black Box of Businesses

S. T. Chava, S. B. S. Venkata, D. Devaraj

   Recommender System with Advanced ratings

J. C. Menchavez, V. Garg, N. Yadav

   Hybrid Recommender System: Recommending Restaurants to Users

D. Ghoghari, L. Kejriwal, R. Kumar

   Predicting the most profitable area a new/existing business can expand to

A. Chavan, L. Dharmapuri, V. S. P. Nallamalli, T. Joshi

   Recommendation System with Aspect-Based Sentiment Analysis

Q. Du, D. Zhu, W. Duan

   Where should I go for Breakfast/Lunch/Dinner?

Z. Xie, Y. Zhu, X. Shen, Y. Wang

   Tinder Eats: Bring people and food together!

A. Adiga, A. Narang, A. Mohan

   Predicting the Success of New, Upcoming Restaurants

V. Chenna, S. Deviah, K. Mukherjee, R. Rayala

   Yelp Recommendation Based on Aggregated Ratings from Sentiment and Stars

J. Lee, D. Goodfellow, T. Do

   Smart Recommender

P. Govindappa, S. Udeda, V. Selvaraj, L. Zhang