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The 13th International Conference on Knowledge Discovery and Data Mining

Statistical Modeling of Relational Data

author: Pedro Domingos, University of Washington

Description

KDD has traditionally been concerned with mining data from a single relation. However, most applications involve multiple interacting relations, either explicitly (in relational databases) or implicitly (in semi-structured and multimodal data). Examples include link analysis, social networks, bioinformatics, information extraction, security, ubiquitous computing, etc. Mining such data has become a topic of keen interest in the KDD community in recent years. The key difficulty is that data in relational domains is no longer i.i.d. (independent and identically distributed), greatly complicating statistical modeling. However, research has now advanced to the point where robust, easy-to-use, general-purpose techniques and languages for mining non-i.i.d. data are available. The goal of this tutorial is to add a sufficient subset of these concepts and techniques to the toolkits of both researchers and practitioners.

Categories

Top: Computer Science: Machine Learning: Inductive Logic Programming

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*Slides*
0:03	Statistical Modeling Of Relational Data
0:09	Overview pt 1
0:38	Motivation
3:37	Examples
4:45	Costs and Benefits of Multi-Relational Data Mining
7:27	Goal and Progress
8:55	Plan
10:56	Disclaimers
12:10	Overview pt 2
12:14	Markov Networks pt 1
15:33	Markov Networks pt 2
17:05	Markov Nets vs. Bayes Nets
19:20	Inference in Markov Networks
20:41	MCMC: Gibbs Sampling
21:47	Other Inference Methods
22:49	MAP/MPE Inference
23:44	MAP Inference Algorithms
24:59	Overview pt 3
25:07	Learning Markov Networks
25:38	Generative Weight Learning
28:06	Pseudo-Likelihood
30:02	Discriminative Weight Learning
32:25	Other Weight Learning Approaches
32:51	Structure Learning
34:23	Overview pt 4
34:27	First-Order Logic
36:09	Inference in First-Order Logic
36:50	Satisfiability
38:29	Stochastic Local Search
39:10	The WalkSAT Algorithm
40:28	Overview pt 5
40:40	Rule Induction
42:02	Learning a Single Rule
42:43	Learning a Set of Rules
44:10	First-Order Rule Induction
46:46	Overview pt 5
47:00	Plethora of Approaches
47:25	Key Dimensions
48:51	Knowledge-Based Model Construction
50:45	Stochastic Logic Programs
52:14	Probabilistic Relational Models
53:59	Relational Markov Networks
55:09	Bayesian Logic
57:20	Markov Logic pt 1
58:25	Markov Logic pt 2

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