Browsed by
Tag: graphical models

The 1-D Cluster Variation Method (CVM) – Simple Application

The 1-D Cluster Variation Method (CVM) – Simple Application

Comments 2 comments

The 1-D Cluster Variation Method – Application to Text Mining and Data Mining There are three particularly good reasons for us to look at the Cluster Variation Method (CVM) as an alternative means of understanding the information in a system: The CVM captures local pattern distributions (for an equilibrium state), When the system is made up of equal numbers of units in each of two states, and the enthalpy for each state is the same (the simple unit activation energy…

Read More Read More

Big Data, Big Graphs, and Graph Theory: Tools and Methods

Big Data, Big Graphs, and Graph Theory: Tools and Methods

Comments 0 Comment

Big Graphs Need Specialized Data Storage and Computational Methods {A Working Blogpost – Notes for research & study} Processing large-scale graph data: A guide to current technology, by Sherif Sakr (ssakr@cse.unsw.edu.au), IBM Developer Works (10 June 2013). Note: Dr. Sherif Sakr is a senior research scientist in the Software Systems Group at National ICT Australia (NICTA), Sydney, Australia. He is also a conjoint senior lecturer in the School of Computer Science and Engineering at University of New South Wales. He…

Read More Read More

Graph Theory — Becoming "Organizing Framework"

Graph Theory — Becoming "Organizing Framework"

Comments 0 Comment

Something I’ve been noting — both on my own, and in conversations with Jenn Sleeman , who’s in touch with the academic world at UMBC — Graph theory is growing in centrality as a fundamental organizing framework for many current and emerging computational processes. Specifically, anything more complex than a simple “tuple” (RDF or OWL, etc.), needs to be matched against a graph or partial graph. One good “integrative” paper is Understanding Belief Propagation and its Generalizations by J.S. Yedidia,…

Read More Read More