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).

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Lecture Slides

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)

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Example Programs

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

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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

Comparison of Clustering Methods using YELP dataset

A. Nagpal, A. Ordabekov

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