EECS4412

Handouts
*General
active reading
paper writing
representational typology
introduction to machine learning concepts
heuristics
logic
data mining tasks
rough sets introduction

*Early data mining - the DBLEARN family
Yandong Cai's thesis - DBLEARN
Learning in relational databases: an attribute-oriented approach
Knowledge Discovery in Databases: An Attribute-Oriented Approach
The Software Architecture of DBLEARN
Performance Improvement in the Implementation of DBLEARN
Tony Hu's MSc thesis - Conceptual Clustering
Tony Hu's PhD thesis - DBROUGH
Learning in Relational Databases: a Rough Set Approach
A Tutorial Guide to DBDISCOVER
Data Visualization in the DB-Discover System
RIAC: A Rule Induction Algorithm Based on Approximate Classification
Performance Evaluation of Attribute-Oriented Algorithms for Knowledge Discovery from Databases: Extended Report
DBROUGH: A Rough Set Based Knowledge Discovery System
Mining Knowledge Rules flom Databases: A Rough Set Approach
Rough Sets Similarity-Based Learning from Databases

*Some stuff on rough sets
Very Gentle Introduction to Rough Sets
Noisy Data in Rough Sets
Rough Sets: A Tutorial - Komorowski et al.
Rough Set Rudiments - Pawlak et a.
Tutorial Rough sets theory - Walczak et al.
Rough Sets -Pawlak
An Introduction to Rough Set Theory and Its Applications: A tutorial - Suraj

*Some stuff on ELEM2
ELEM2: A Learning System for More Accurate Classifications
Discretization of Continuous Attributes for Learning Classication Rules
Rule Quality Measures Improve the Accuracy of Rule Induction
ELEM2 Rule Induction System Manual
Rule-Induction and Case-Based Reasoning
From Computational Intelligence to Web Intelligence

*What every computer scientist should know
A Basic Introduction to Neural Networks
Tutorial- Basic Genetic Algorithm in plain English
When to use (not use) neural networks
Greedy algorithm
Simulated annealing
Hill climbing
Tabu search
Beam search
np
decision trees
gradient decent


*EM Algorithm
Tutorial on the EM Algorithm and its Application
What is the EM Algorithm
EM Talk
EM Algorithm slides
Expectation-Maximization and Hidden Markov Models
Applying the extended mass-constraint EM algorithm to image retrieval


*Recommender Systems
Introduction to Recommender Systems
How Computers Know What We Want — Before We Do
Recommender Systems
Next Generation Recommender Systems
Evaluating Collaborative Filtering Recommender Systems
Evaluation of Recommender Systems
Hybrid Recommender Systems


*Spatial Data Mining Systems
Introduction to Spatial Databases
Mining Object, Spatial, Multimedia, Text, and Web Data
A Unified Approach to Spatial Outliers Detection
Discovering Spatial Co-location Patterns
Spatial Data Mining - Accomplishments


*Miscellaneous**
data mining glossary
decision trees
K NN algorithm
Bag-of-words representation
Common n-gram classification
Top 10 algorithms in data mining