'Means method' presentation slideshows

Classification & Clustering

Classification & Clustering. Pieter Spronck http://www.cs.unimaas.nl/p.spronck. Binary Division of Marbles. Big vs. Small. Transparent vs. Opaque. Marble Attributes. Size (big vs. small) Transparency (transparent vs. opaque) Shininess (shiny vs. dull)

753 views • 60 slides

Clustering

Clustering. Instructor: Qiang Yang Hong Kong University of Science and Technology Qyang@cs.ust.hk Thanks: J.W. Han, I. Witten, E. Frank. Essentials. Terminology: Objects = rows = records Variables = attributes = features A good clustering method

716 views • 54 slides

INSTITUTIONALISM

748 views • 29 slides

K-Means Clustering

K-Means Clustering. What is Clustering?. Also called unsupervised learning , sometimes called classification by statisticians and sorting by psychologists and segmentation by people in marketing. Mengelompokkan data-data menjadi beberapa cluster berdasarkan kesamaannya.

557 views • 28 slides

Livelihoods analysis using SPSS

399 views • 17 slides

Cluster Analysis

Cluster Analysis. Classifying the Exoplanets. Cluster Analysis. Simple idea, difficult execution Used for indexing large amounts of data in databases. (very hot skill to have 70/hour)

227 views • 12 slides

TRAIN

TRAIN. Y_TRAIN. X. X_TRAIN: 114*2. D=cross_distance(X,centers) H=exp(-D/200)*a Y_HAT=H>0.5. centers=my_kmeans[X_TRAIN,3,]. D=cross_distance(X_TRAIN,centers). a=pinv(exp(-D))*Y_TRAIN. Y_HAT. TEST. X. D= cross_distance ( X,centers ). centers. H=exp(-D/200)*a). a. Y_HAT=H>0.5. Y_HAT.

166 views • 3 slides