Clustering of Cities by Craigslist Posts Charles Johnson and Michael Kim, Stanford University - CSDecember 2012 e aimed to evaluate the similarity between US cities by clustering them based on postings from the c

Source URL: cs229.stanford.edu

Research Challenge 1 Graphs with target clustering coefficient and given input degree sequences Speaker: Arnau Prat Pérez Context and motivation: Generating graphs with realistic structural characteristics is of high

Source URL: edbt2015school.win.tue.nl

A Semi-automatic Method for Efficient Detection of Stories on Social Media Soroush Vosoughi, Deb Roy Massachusetts Institute of Technology Cambridge, MA 02139 ,

Source URL: socialmachines.media.mit.edu

• Software
• Computing
• Information science
• Cluster analysis
• Document clustering
• Twitter
• Hierarchical clustering
• Dendrogram
• Social media

Revealing properties of the bipartite structure of online social networks Raphaël TACKX, Jean-loup GUILLAUME,

Source URL: rcis2015.hua.gr

• Network theory
• Graph theory
• Operations research
• Clustering coefficient
• Social network
• Assortativity
• Bipartite graph
• Complex network
• Climate as complex networks

1 Clustering Music by Genres Using Supervised and Unsupervised Algorithms Kyuwon Kim, Wonjin Yun, and Rick Kim

Source URL: cs229.stanford.edu

Submitted by Brian Sa and Patrick Shih for CS229 FallK-MEANS FOR NETFLIX USER CLUSTERING I. Introduction and model unsupervised method for classifying users in the vast

Source URL: cs229.stanford.edu

Sensing and Predicting the Pulse of the City through Shared Bicycling Jon Froehlich1, Joachim Neumann2, Nuria Oliver2 1 Computer Science and Engineering University of Washington

Source URL: www.nuriaoliver.com

• Computing
• Prediction
• Business
• Sustainable transport
• Cluster analysis
• Data mining
• Geostatistics
• Cycling
• Bicing
• Hierarchical clustering
• Computer cluster
• Forecasting

Application of Clustering for Unsupervised Language Learning Jeremy Hoffman and Omkar Mate Abstract We describe a method for automatically learning word similarity from a corpus. We constructed feature vectors for words

Source URL: cs229.stanford.edu

Segmentation of 3D Meshes through Spectral Clustering Rong Liu Hao Zhang GrUVi Lab, School of Computing Science

Source URL: www1.idc.ac.il

Stream Clustering: Efficient Kernel-based Approximation using Importance Sampling Radha Chitta, Rong Jin and Anil K. Jain Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824,

Source URL: www.cse.msu.edu