Functional Subspace Clustering with Application to Time Series A. Proof of the statement in Eq. (3) In order to show the the result in Eq. (3), we breakdown P the process in Eq. (2) into two steps: Let us denote e =

Source URL: jmlr.org

• Mathematics
• Functions and mappings
• Transformation
• Abstract algebra
• Linear algebra
• Linear map
• Quaternions

Generic Promotion of Diffusion-Based Salient Object Detection Peng Jiang 1 Nuno Vasconcelos 2 Jingliang Peng 1 ∗

Source URL: www.cv-foundation.org

• Algebra
• Mathematics
• Linear algebra
• Matrix theory
• Algebraic graph theory
• Computer vision
• Image processing
• Saliency map
• Eigenvalues and eigenvectors
• Spectral clustering
• Eigendecomposition of a matrix
• Laplacian matrix

Scalable Graph Clustering with Pregel Bryan Perozzi1 , Christopher McCubbin2 , Spencer Beecher3 , and J.T. Halbert3 Abstract We outline a method for constructing in parallel a collection of local clusters for a massive d

Source URL: sqrrl.com

Proceedings of the 14th International Conference on Auditory Display, Paris, France June, 2008 DURCHEINANDER UNDERSTANDING CLUSTERING VIA INTERACTIVE SONIFICATION Till Bovermann1 , Julian Rohrhuber2 , Helge Rit

Source URL: tuio.lfsaw.de

• Statistics
• Cluster analysis
• Information science
• Data analysis
• Dendrogram
• Graph drawing
• Sonification
• Clustering
• Hierarchical clustering
• Single-linkage clustering

Audio Sources Separation by Clustering Techniques Wei Fang [Abstract] This project tries to present a method to distinguish different components in a piece of audio signal without prior knowledge of instrument types and

Source URL: cs229.stanford.edu

Spectral Clustering via the Power Method - Provably Christos Boutsidis Yahoo, 229 West 43rd Street, New York, NY, USA. BOUTSIDIS @ YAHOO - INC . COM

Source URL: jmlr.org

• Algebra
• Mathematics
• Linear algebra
• Matrix theory
• Algebraic graph theory
• Spectral clustering
• Eigenvalues and eigenvectors
• Graph partition
• Cluster analysis
• Laplacian matrix
• Singular value decomposition
• K-means clustering

Pragmatic Text Mining: Minimizing Human Effort to Quantify Many Issues in Call Logs

Source URL: www.kirshenbaum.net

• Machine learning
• Statistical classification
• Data mining
• Cluster analysis
• Geostatistics
• K-means clustering
• Pattern recognition
• Support vector machine
• Supervised learning
• Naive Bayes classifier
• Determining the number of clusters in a data set
• Semi-supervised learning

Blind, Greedy, and Random: Algorithms for Matching and Clustering using only Ordinal Information Elliot Anshelevich Shreyas Sekar

Source URL: www.cs.rpi.edu

• Matching
• Combinatorics
• NP-complete problems
• Combinatorial optimization
• Spanning tree
• Stable marriage problem
• Approximation algorithm
• 3-dimensional matching
• Minimum spanning tree

Bayesian integrative clustering for genomics datasets Evelina Gabasova (), Lorenz Wernisch, John Reid Mixture model for multiple contexts

Source URL: s3-eu-west-1.amazonaws.com

• Gene expression
• Cluster analysis
• Data mining
• Geostatistics
• MicroRNA
• DNA methylation

Language-Independent Hierarchical Clustering of Web Pages using Content Mining and Phrase Extraction Ricardo Campos 1 1 Centre

Source URL: www.ccc.ipt.pt

• Data mining
• Cluster analysis
• Hierarchical clustering
• Web mining
• Bioinformatics