31108008-cluster analysis.ppt

Transcript 31108008-cluster analysis.ppt

Telecommunications Industry Association Telecon July 12, 2011 TR-30.3/11-07-008

Document Cover Sheet Project Number

PN-3-0062-RV2

Document Title Source TIA-921B Preliminary HMM Cluster Analysis

Alcatel-Lucent

Contact Distribution Intended Purpose of Document (Select one)

Les J. Wu Alcatel-Lucent 600 Mountain Avenue Room 2b-219 Murray Hill, NJ 07974 Phone: 908-582-4964 Fax: 908-582-4964 Email: [email protected] TR-30.3 X For Incorporation Into TIA Publication For Information Other (describe) -

The document to which this cover statement is attached is submitted to a Formulating Group or sub-element thereof of the Telecommunications Industry Association (TIA) in accordance with the provisions of Sections 6.4.1–6.4.6 inclusive of the TIA Engineering Manual dated October 2009, all of which provisions are hereby incorporated by reference. Abstract

TIA-921B Preliminary HMM Cluster Analysis

Les J. Wu July 2011

G1050 Examples

(Counter results from Telchemy vqcapture alpha)

What does it all mean?

• -

Probabilities computed from counts.

.xls previously submitted available as contribution 7 ftp://[email protected]/TR-30/TR-30.3/Public/1104-Austin/31106007-markov%20model.xls

•

Is there clustering of variables or computed parameters?

• Very Few Errors in VoIP cases with errors Many Px probabilities could not be extracted due to P32=0

Dataset has had an initial look using statistical package “R”

• Principle Component Analysis is similar to Eigenvalue Decomposition or SVD .csv and commands used in analysis in backup section of this presentation

By definition, some of the model parameters are correlated

Pairwise P-Plot

•

P1+P2+P3 = 1

•

Color coding:

Red=HD

Blue=SD

Green=VoIP

Hmm[,18:20]

Comp.1 Comp.2 Comp.3

Standard deviation 0.04214965 0.01097286 3.577409e-06 Proportion of Variance 0.93652931 0.06347068 6.746376e-09 Cumulative Proportion 0.93652931 0.99999999 1.000000e+00 Loadings: Comp.1 Comp.2 Comp.3

p1 0.780 0.242 0.577

p2 -0.180 -0.796 0.577

p3 -0.600 0.554 0.577

Hmm[,10:16]

Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 0.8137195 0.15383318 0.028077692 3.643808e-03 Proportion of Variance 0.9643663 0.03446613 0.001148194 1.933764e-05 Cumulative Proportion 0.9643663 0.99883247 0.999980662 1.000000e+00 Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7

p11 -0.703 0.707

p13 0.703 0.707

Comp.1 Comp.2 Comp.3 Comp.4

Standard deviation 7.462114e+05 3.534456e+03 Proportion of Variance 9.999772e-01 2.243425e-05 Cumulative Proportion 9.999772e-01 9.999996e-01 Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6

c11 1.000 c13 0.147 0.954 -0.258

c14 0.101 0.925 0.366

c22 -0.931 -0.365 c23 -0.242 0.379 -0.893

Wireless

Wireless Uplink TCP Goodput

Fairfax, Va – 2 July 2011

Loaded Uplink Delay Histogram

LTE/WiFi Delay Distribution

Hotel Wifi vs. 3G Goodput

LTE TCP Transaction

Backup

R Commands

> hmm<-read.csv("hmm.csv") > vec<-c("red", "red", "blue", "blue", "green", "green") > class<-unclass(hmm$file) > class2<-unclass(hmm$codec) > vec2=c("blue", "red", "green") > class > class2 > pairs(na.omit(hmm[10:16]), pch=21, bg=vec[class]) > pairs(na.omit(hmm[17:20]), pch=21, bg=vec[class]) > hmm.prc<-princomp(na.omit(hmm[,10:16])) > summary(hmm.prc, loadings=TRUE) > pairs(hmm.prc$scores, pch=21, bg=vec[class]) > hmm.prc<-princomp(na.omit(hmm[,18:20])) > summary(hmm.prc, loadings=TRUE) > pairs(hmm.prc$scores, pch=21, bg=vec[class]) > plot(hmm.prc$scores[,1:2], pch=21, bg=vec2[class2],main="r-video b-audio") > plot(hmm.prc$scores[,1:2], pch=21, bg=vec[class],main="r-HD b-SD") > plot(hmm.prc$scores[,1:2], pch=21, bg=vec[class]) > text(hmm.prc$scores[,1:2], labels=class, pos=4) > vec<-c("red", "red", "blue", "blue", "green", "green") > pairs(hmm.prc$scores, pch=21, bg=vec[unclass(hmm$file)]) case Dw1 Dw2 Dw3 Dw4 Dw5 Dw6 Dw7 Dw8 Gw1 Gw2 Gw3 Gw4 Gw5 Gw6 Gw7 Gw8 Dw3 Gw3 Dp1 Dp2 Dp3 Dp4 Dp6 Dw2 Dw4 Dw5 Dw6 Dw7 Gp1 Gp2 Gp3 Gp1 Gp2 Gp3 Gp4 Gp5 Gp6 Gw4 Gp4 Gp6 Gw2 Gw4 Gw5 Gw6 Gw7 Dp1 Dp2 Dp3 Dp4 Dp5 Dp6 Dw4 Dw6 Dw7 file

Save Me as hmm.csv:

codec 12804 12808 12807 12807 12806 12806 17898 17875 17806 16954 17351 17062 17895 17839 17803 17899 17898 17897 17897 17890 17893 17897 c11 86939 86965 86960 86943 86829 86848 86842 86790 86979 86977 86973 86970 86968 86964 86962 86957 100179 100186 12807 12769 12754 12021 12047 12804 12793 12734 12761 12737 12808 12808 12807 12807 c13 105 117 90 100 121 117 117 126 168 168 168 168 168 167 168 168 144 155 2 2 191 172 163 169 156 178 175 174 124 124 124 124 156 156 156 140 156 156 124 124 124 124 124 124 156 141 141 163 153 162 150 147 145 156 c14 92 89 90 103 22 18 22 32 0 0 0 0 0 3 0 1 83 18 7 0 27 72 69 20 73 38 26 7 24 24 24 24 15 15 15 21 15 15 24 24 24 24 24 24 15 31 30 109 116 107 19 24 28 15 c22 27 5 0 15 50 33 32 63 0 0 0 0 0 0 0 0 45 641 607 0 0 25 0 0 12 63 31 55 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 8 68 717 362 616 0 29 59 0 c23 2 1 0 2 7 7 5 7 0 0 0 0 0 0 0 0 0 0 0 5 83 4 82 0 2 7 4 7 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2 86 7 59 81 0 5 9 0 c33 661 648 678 655 p11 0.99879

p13 0.00121

p31 0.13672

p32 0.0026

p33 0.86068

p22 0.93103

0.99866

0.99897

0.99885

0.99861

0.00134

0.00103

0.00115

0.00139

0.15274

0.11719

0.1321

0.15633

0.00131

0 0.00264

0.00904

0.84595

0.86526

0.83463

0.83333

0.88281 NA 0.88235

0.87719

646 649 653 646 0.99865

0.99865

0.99855

0.99807

0.00135

0.00145

0.00193

0.15136

0.15097

0.16175

0.21622

0.00906

0.00645

0.00899

0 0.83959

0.84258

0.82927

0.78378 NA 0.825

0.86486

0.9

609 609 609 609 0.99807

0.99807

0.00193

0.21622

0 0 0 0 0.78378 NA 0.78378 NA 0.78378 NA 0.78378 NA 609 609 609 609 0.99808

0.99807

0.99998

0.00192

0.00193

0.00002

0.21521

0.21622

0.5

0 0 0 0 0.78479 NA 0.78378 NA 0.78378 NA 0.5 NA 899 961 2 2 0.99998

0.98888

0.98801

0.98525

0.00002

0.01112

0.01199

0.01475

0.5

0.13806

0.13827

0.17269

0 0 0.00446

0.00362

0.5 NA 0.86194 NA 0.85727

0.82369

0.83333

0.91837

911 930 942 940 0.98589

0.98665

0.98697

0.98795

0.01411

0.01335

0.01303

0.01205

0.14515

0.13732

0.15239

0.14801

0.07004

0.06908

0 0.0019

0.78481

0.7936

0.85009

0.88536

0.88099

0.84761 NA 0.85714

896 912 934 951 0.98621

0.98647

0.98652

0.99041

0.01379

0.01353

0.01348

0.00959

0.16226

0.15723

0.15371

0.11262

0.00638

0.00359

0.00618

0 0.83136

0.83917

0.84011

0.88738 NA 0.9

0.88571

0.8871

977 977 977 977 0.99041

0.99041

0.00959

0.11262

0 0 0 0 0.88738 NA 0.88738 NA 0.88738 NA 0.88738 NA 977 977 977 977 0.99041

0.99041

0.00959

0.11262

0 0 0 0 0.88738 NA 0.88738 NA 0.88738 NA 0.88738 NA 977 977 775 786 0.99041

0.99136

0.99217

0.99214

0.00959

0.00864

0.00783

0.00786

0.11262

0.16756

0.15178

0.15032

0 0 0.00215

0.00746

0.88738 NA 0.83244 NA 0.84607

0.84222

0.8

0.90667

790 811 800 797 0.99048

0.99126

0.99059

0.99169

0.00952

0.00874

0.00941

0.00831

0.15377

0.15119

0.15577

0.16164

0.08113

0.0583

0.07788

0 0.76509

0.79051

0.76635

0.83836 NA 0.8929

0.85986

0.88379

778 797 796 774 0.99183

0.99192

0.99136

0.00817

0.00808

0.00864

0.1549

0.15263

0.16774

0.00527

0.00947

0 0 0.83983

0.83789

0.85294

0.86765

0.83226 NA 0.83226 NA 774 774 774 784 774 774 0.99136

0.99136

0.99224

0.99136

0.00864

0.00776

0.00864

0.16774

0.15119

0.16774

0 0 0.00216

0 0 0.83226 NA 0.83226 NA 0.84665

0.66667