2012 BayesianRelationalDataAnalysis

• (Ueda, 2012) ⇒ Naonori Ueda. (2012). “Bayesian Relational Data Analysis.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339659

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Bayesian+Relational+Data+Analysis
• http://dl.acm.org/citation.cfm?id=2339530.2339659&preflayout=flat#citedby

Quotes

Author Keywords

• Bayesian analysis; data mining; relational data

Abstract

Recently there have been many collections of relational data in diverse areas such as the internet, social networks, customer shopping records, bioinformatics, etc. The main goal of the relational data analysis is to discover latent structure from the data. The conventional data mining algorithms based on exhaustive enumeration have an inherent limitation for this purpose because of the combinatorial nature of the methods. In contrast, in machine learning a lot of statistical models have been proposed for the relational data analysis. In this talk, first I will review the statistical approach, especially Bayesian approach, for the relational data analysis with recent advancements in machine learning literature. Then, as a future research I will also talk about a statistical approach for combining multiple relational data.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 BayesianRelationalDataAnalysis	Naonori Ueda			Bayesian Relational Data Analysis				10.1145/2339530.2339659		2012