'K clusters' presentation slideshows

Christopher M. Bishop

Latent Variables, Mixture Models and EM. Christopher M. Bishop. Microsoft Research, Cambridge. BCS Summer School Exeter, 2003. Overview. K-means clustering Gaussian mixtures Maximum likelihood and EM Probabilistic graphical models Latent variables: EM revisited

1.72k views • 101 slides

Text Document Clustering

Text Document Clustering. C. A. Murthy Machine Intelligence Unit Indian Statistical Institute. Text Mining Workshop 2014. What is clustering?. Clustering provides the natural groupings in the dataset. Documents within a cluster should be similar.

738 views • 51 slides

Optimization problems

Optimization problems. INSTANCE FEASIBLE SOLUTIONS COST . Vertex Cover problem. INSTANCE graph G FEASIBLE SOLUTIONS S ? V, such that ( ? e ? E) S ? e ?0 COST c(S) = |S|. Set Cover problem. INSTANCE family of sets A 1 ,...,A n ? ? FEASIBLE SOLUTIONS

680 views • 47 slides

Clustering

Clustering. Idea and Applications. Clustering is the process of grouping a set of physical or abstract objects into classes of similar objects. It is also called unsupervised learning. It is a common and important task that finds many applications. Applications in Search engines:

593 views • 46 slides

Clustering

Clustering. Outline. Microarrays Hierarchical Clustering K-Means Clustering Corrupted Cliques Problem CAST Clustering Algorithm . Applications of Clustering. Viewing and analyzing vast amounts of biological data as a whole set can be perplexing

1.12k views • 45 slides

Clustering Gene Expression Data: The Good, The Bad, and The Misinterpreted

Clustering Gene Expression Data: The Good, The Bad, and The Misinterpreted. Elizabeth Garrett-Mayer November 5, 2003 Oncology Biostatistics Johns Hopkins University esg@jhu.edu. Acknowledgements: Giovanni Parmigiani, David Madigan, Kevin Coombs. Data from Garber et al. PNAS (98), 2001.

578 views • 44 slides

Clustering analysis of microarray gene expression data

Clustering analysis of microarray gene expression data. Ping Zhang November 19 th , 2008. Outline. Gene expression Similarity between gene expression profiles Concept of clustering K-Means clustering Hierarchical clustering Minimum spanning tree-based clustering.

902 views • 44 slides

Tutorial on Data Mining

Tutorial on Data Mining. Workshop of the Indian Database Research Community Sunita Sarawagi School of IT, IIT Bombay. Data mining. Process of semi-automatically analyzing large databases to find interesting and useful patterns

554 views • 43 slides

Cluster analysis ? Function

Cluster analysis ? Function. Places genes with similar expression patterns in groups. Sometimes genes of unknown function will be grouped with genes of known function. The functions that are known allow the investigator to hypothesize regarding the functions of genes not yet characterized.

530 views • 42 slides

Why do we want a good ratio anyway? Approximation stability and proxy objectives

Why do we want a good ratio anyway? Approximation stability and proxy objectives. Avrim Blum Carnegie Mellon University Based on work joint with Pranjal Awasthi , Nina Balcan, Anupam Gupta, Or Sheffet , and Santosh Vempala.

419 views • 35 slides

The Clustering Problem

458 views • 34 slides

Clustering

Clustering. Hypothesis: Hebbian synaptic plasticity enables a perceptron to compute the mean of its preferred stimuli. Unsupervised learning. Sequence of data vectors Learn something about their structure Multivariate statistics Neural network algorithms Brain models.

444 views • 32 slides

Clustering Techniques and their applications at ECMWF

Clustering Techniques and their applications at ECMWF. Susanna Corti European Centre for Medium-Range Weather Forecasts. Outline. Cluster analysis - Generalities Metrics Clustering techniques Suitable (sub)spaces of states K-means method Significance Number of clusters Examples

539 views • 32 slides

A UNIQUENESS THEOREM FOR CLUSTERING

452 views • 26 slides

BIONFORMATIC aLGORITHMS

300 views • 21 slides

Feedback from LAV for the technical run data

Feedback from LAV for the technical run data . T. Spadaro. Run conditions - summary. Running with LAV stations 1 and 2: 1 channel / 320 could not be switched on (HV connector problem at flange) 1 channel / 320 frequently tripping (divider current not stable)

303 views • 17 slides

Chapter 4 Clustering

Chapter 4 Clustering . What is Clustering?. The process of organizing objects into groups whose members are similar in some way Statistics, machine learning, and database researchers have studied data clustering Recent emphasis on large datasets. Approaches to Clustering.

299 views • 15 slides

Overview of Machine Learning

Overview of Machine Learning. RPI Robotics Lab Spring 2011 Kane Hadley. Agenda. What is Machine Learning? Some techniques Simple Implementations Implementations for complex problems.

268 views • 14 slides

Spectral Clustering

Spectral Clustering. Scatter plot of a 2D data set. K-means Clustering. Spectral Clustering. U. von Luxburg . A tutorial on spectral clustering. Technical report, Max Planck Institute for Biological Cybernetics, Germany, 2006. Conventional K-means Clustering.

1.27k views • 14 slides

File Systems

File Systems. comparison of FAT, NTFS, and Linux. Terms. Cluster smallest amount of space that can be allocated to hold a file big cluster sizes lead to wasted space, while small cluster sizes overburden the directory system cluster sizes range from 1 sector (.5K) to 128 sectors (64K)

220 views • 9 slides