Machine Learning Topics

Particle Swarm Optimization
wikipedia
Swarm Intelligence

Ant Algorithms
ant colony optimization

Reinforcement Learning
wikipedia
Q-learning
Q-learning definition
Markov decision process

Computational Learning Theory
wikipedia
VC dimension
Principle of maximum entropy

Ensembles, Bagging and Boosting
Boosting

Meta-Learning
METAL KDD
Christophe Giraud-Carrier

HMMs
Hidden Markov model

Viral Marketing

Dr. Ralph F. Wilson suggests that Viral Marketing is comprised of the following components:
1. Gives away products or services
2. Provides for effortless transfer to others
3. Scales easily from small to very large
4. Exploits common motivations and behaviors
5. Utilizes existing communication networks
6. Takes advantage of others' resources
The effects of word-of-mouth, or viral marketing are motivations for utilizing the social network that customers belong in.

Customer Segmentation

Customer analysis helps a business better meet customer needs. Learning more about your customers is often benefited by intelligent segmentation. Customers can be segmented into a variety of groups. These segments can be based on behavioural, demographic, geographic, and psychographic variables. In fact customers can be segmented by any combination of these variables. Through viewing customers within such segments the problem of identifying and serving customers is simplifed. The knowledge provided by these segments is usually useful for determining actionable marketing tactics.

Stanford Data Mining Course

Stanford offers a nice Data Mining and Electronic Business course within the Statistics department. It looks like it covers many exciting aspects of the field.

What is Lift?

In data mining, "lift" is often used to measure model performance. Here is a link to an article that explains how it is used: DMReview article

IP Country Lookup Tool

Here is a link to a tool that I created to lookup the countries for all of the IP addresses in a mess of text.

Idea: Transaction Logger

It would be great to create a device that logs all of my transactions (whatever the method used to purchase) that could be carried around while shopping. This would then enable consumers to do personal data-mining on all of their own transactions. This would be a unique tool to help consumers improve purchasing habits and make smarter decisions. Additional interesting product associations could also be calculated and analyzed.

Web Data Mining (for Business Intelligence)

Bamshad Mobasher teaches a nice Web Mining course entitled "Web Data Mining (for Business Intelligence)" at DePaul University in Illinois. Currently, it is one of the few courses dedicated solely to this topic. I expect, as time goes, the number of courses on this topic will grow dramatically.

Exploring Bayesian Methods

Bayesian methods can be used to deal with uncertainty.
Here are some links that help to explore the area:
Bayesian Inference
Empirical Bayes
Hierarchal Bayes
Bayesian Network
Bayes' theorem

Statistics Topics
Expected Value
Likelihood
Mean
Variance
Mean Squared Error (MSE)
Posterior Probability
Conditional, Joint, and Marginal Probability
Utility Functions (Link  2)

Distributions
Normal
Gamma
Poisson
Beta
Binomial
Conjugate Prior

Other Related Topics
Markov Chain Monte Carlo (MCMC)
Simulated Annealing
Tabu Search (Link 2)
Kalman Filter (Link 2)
Particle Filter
Directed Acyclic Graphs (DAG)
Markovian Random Field (MRF)
EM Algorithm (Bayesian Structural EM - Friedman)
Reading List of Bayesian Methods

Helpful Software
JavaBayes
Graphviz

Useful Java Libraries
Colt
Tomato

Web Content Mining: Bing Liu

Bing Liu from the University of Chicago is very interested in Web Content Mining. He compiled of list of references regarding the topic. In addition, he gave a tutorial on Web content mining in Chiba, Japan in May, 2005.

Natural Language Processing

Here is a nice introduction and dictionary for Natural Langauge Processing (NLP). This reference might come in handy when mining text documents.

Link to Tingshao Zhu's Web Mining Research

Personalized Assistance System

A researcher, from Penn State, has been working on a personalized assistance system that automatically helps users find more relevant search results (see the article).

Stages of Knowledge Discovery in Websites

                                          |------------------
                     |--------------------| 3. PERSONALIZATION
|---------------------| 2. Advanced Web Mining
| 1. Clickstream Analysis

Personalization Companies

Rule-based Personalization:
ATG, BroadVision, Epiphany, Blue Martini

Collaborative Filtering Personalization:
Amazon.com

Statistical Modeling:
Touch Clarity

State-based Personalization:
Xamplify

Web Mining Research Links by Tingshao Zhu

Social Network Analysis

The Oracle of Bacon

Erdös Number Project

Six Degrees of Separation Experiment

ReferralWeb

Scale-Free Networks

Most social networks including the Web seem to be scale-free networks. A Scale-free network is unique because a small number of nodes are highly connected (e.g., the majority of nodes are sparsely connected).

Wikipedia Encyclopedia

Data Mining Researchers

Rakesh Agrawal
Surajit Chaudhuri
Umesh Dayal
Max J. Egenhofer
Usama Fayyad (Microsoft)
Christophe Giraud-Carrier
Jiawei Han
Daniel Keim
Hans-Peter Kriegel
Yike Guo
Laks V.S. Lakshmanan
Hongjun Lu
Alberto Mendelzon
Raymond T. Ng
Tamer Ozsu
Rajeev Rastogi
Ken Ross
Sunita Sarawagi
Wei-Min Shen
Kyuseok Shim
Avi Silberschatz
Matt Smith
Jaideep Srivastava
Philip S. Yu
Clement Yu
Jeffrey D. Ullman
Ke Wang
Osmar Zaiane

24 Key Database Marketing Techniques

This article is a good overview of 24 marketing methods which are useful in determining what data should be collected in order to personalize a website.

Collaborative Filtering Resources

Collaborative Filtering (Berkeley)

Data Mining Resources

Data Mining Resources (@ www.scd.ucar.edu)

Data Mining Resources (@ www.cs.purdue.edu)

Data Mining Resources (Zillman's List)

Knowledge Discovery Approaches

Data Mining enables us to automatically sift through mass amounts of data to discover KNOWLEDGE. A couple popular approaches are summarized below:

Identify customer groups and forecast their behaviour. This is commonly used in Marketing, fraud detection, and more and more frequently on the web for various purposes.

Market basket analysis: "If customer bought product P, he or she
is likely to buy product Q and R" (Amazon.com uses this approach)