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28 Pages
ClusterInterpretation
Course: ISM 5219, Fall 2008School: UCF
Rating:
Word Count: 2946
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Cluster K-Means Analysis SAS Program PROC IMPORT OUT= WORK.DATA1 DATAFILE= "C:\Documents and Settings\rhightower\My Documents\Teaching\BIAnalysis\Gadgets.xls" DBMS=EXCEL2002 REPLACE; GETNAMES=YES; Run; data data2; set data1; /* ct cx rd vi ra */ compatibility complexity result demonstrability visibility relative advantage ct=ct1+ct2+ct3+ct4; cx=cx1+cx2+cx3+cx4+cx5+cx6; rd=rd1+rd2+rd3+rd4;...
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Cluster K-Means Analysis
SAS Program
PROC IMPORT OUT= WORK.DATA1 DATAFILE= "C:\Documents and Settings\rhightower\My Documents\Teaching\BIAnalysis\Gadgets.xls" DBMS=EXCEL2002 REPLACE; GETNAMES=YES; Run; data data2; set data1; /* ct cx rd vi ra */ compatibility complexity result demonstrability visibility relative advantage
ct=ct1+ct2+ct3+ct4; cx=cx1+cx2+cx3+cx4+cx5+cx6; rd=rd1+rd2+rd3+rd4; vi=vi1+vi2+vi3+vi4+vi5+vi6; ra=ra1+ra2+ra3+ra4+ra5+ra6+ra7+ra8+ra9+ra10+ra11; ui=ui1+ui2+ui3+ui4+ui5+ui6; if ct=. or cx=. or rd=. or vi=. or ra=. then delete; run; /* Standardize data */ proc standard data=data1 out=data2 mean=0 std=1; var ct cx rd vi fa; /* Set number of clusters to 3 and output the results to a data set called data2
1
*/ proc fastclust maxclusters=3 out=data2; var ct cx rd vi ra ; run; /* Plot a variable indicating use of instant messaging against the clusters */ proc freq; tables d9*cluster; run; /* */ proc candisc data=data2 out=Can noprint; class Cluster; var ct cx rd vi ra ; /* */ proc gplot data=Can; plot Can2*Can1=Cluster; run; plot the data proc candisc creates linear combinations of the data so we can plot them
2
k-Means with 2 Clusters
15:22 Tuesday , September 12, 2006 Replace=FULL The FASTCLUS Procedu re Radius=0 Maxclu s t e r s =2 Maxi t e r = 1 In i t i a l Seeds 45
Clus t e r ct cx rd vi ra 1 4.00000000 6.00000000 4.00000000 8.00000000 16.00000000 2 28.00000000 27.00000000 22.00000000 30.00000000 77.00000000 Cr i t e r i o n Based on Fina l Clus t e r Seeds = 5.6622
Summary
Maximum Dis t ance RMS Std f r om Seed Radius Neares t Dis t ance Between Clus t e r Frequency Devia t i o n to Observa t i o n Exceeded Clus t e r Clus t e r Cent r o i d s 1 104 5.9751 32.0774 2 28.8321 2 212 5.5217 25.2679 1 28.8321
3
Sta t i s t i c s
fo r
Var i ab l e s
Var i ab l e Tota l STD With i n STD R- Square RSQ/(1 - RSQ) ct 6.44124 4.93010 0.416028 0.712410 cx 3.86216 3.86081 0.003870 0.003885 rd 3.45478 3.39829 0.035504 0.036811 vi 3.68555 3.68180 0.005205 0.005232 ra 16.20770 9.83256 0.633132 1.725778 OVER-ALL 8.30198 5.67442 0.534308 1.147344 Pseudo F Sta t i s t i c Approx ima te Expec ted Over - Al l Cubic Clus t e r i n g = 360 .27 0.57342
R- Squared = = fo r - 2.145
Cr i t e r i o n
WARNING: The two va lues above are i n va l i d
cor r e l a t e d var i a b l e s .
4
15:22 Tuesday , September 12, 2006 Replace=FULL The FASTCLUS Procedu re Radius=0 Maxclu s t e r s =2 Maxi t e r = 1 Clus t e r Means
46
Clus t e r ct cx rd vi ra 1 11.88461538 24.19230769 18.44230769 26.55769231 27.19230769 2 20.71226415 24.70283019 19.82547170 27.12264151 54.59433962 Clus t e r Standa rd Devia t i o n s
Clus t e r ct cx rd vi ra 1 4.797466346 4.673986933 4.170695090 4.471760200 9.810908257 2 4.993573174 3.393804155 2.948646129 3.226674458 9.84310888
5
k-Means with 3 Clusters
15:22 Tuesday , September 12, 2006 Replace=FULL The FASTCLUS Procedu re Radius=0 Maxc lus t e r s =3 Maxi t e r = 1 In i t i a l Seeds 41
Clus t e r ct cx rd vi ra 1 4.00000000 18.00000000 14.00000000 18.00000000 44.00000000 2 28.00000000 27.00000000 22.00000000 30.00000000 77.00000000 3 9.00000000 30.00000000 21.00000000 30.00000000 11.00000000 Cr i t e r i o n Based on Fina l Clus t e r Seeds = 4.5119
Summary
Maximum Dis t ance RMS Std f r om Seed Radius Neares t Dis t ance Between Clus t e r Frequency Dev ia t i o n to Observa t i o n Exceeded Clus t e r Clus t e r Cent r o i d s 1 149 4.2335 24.6350 2 20.1874 2 102 4.2273 20.4689 1 20.1874 3 65 5.4302 32.4744 1 23.6908 Sta t i s t i c s fo r Var i ab l e s
Var i ab l e Tota l STD With i n STD R- Square RSQ/(1 - RSQ) ct 6.44124 4.54033 0.506294 1.025495 cx 3.86216 3.81375 0.031103 0.032101
6
rd vi ra OVER-ALL
3.45478 3.68555 16.20770 8.30198
3.29890 3.61481 6.49951 4.50228 =
0.093991 0.044130 0.840209 0.707763 379.02 0.68421
0.103741 0.046167 5.258177 2.421883
Pseudo F Sta t i s t i c Approx ima t e Expec ted Over - Al l Cubic Clus t e r i n g
R- Squared = = fo r 2.169
Cr i t e r i o n
WARNING: The two va l ues above are i n va l i d
cor r e l a t e d var i a b l e s .
7
15:22 Tuesday , September 12, 2006 Replace=FULL The FASTCLUS Procedu re Radius=0 Maxc lus t e r s =3 Maxi t e r = 1 Clus t e r Means
42
Clus t e r ct cx rd vi ra 1 16.81879195 24.05369128 18.64429530 26.33557047 44.24832215 2 23.60784314 25.51960784 20.90196078 28.04901961 62.99019608 3 10.96923077 24.09230769 18.63076923 26.56923077 21.29230769 Clus t e r Standa rd Dev ia t i o n s
Clus t e r ct cx rd vi ra 1 4.790268417 3.830505638 2.892380076 3.098846207 5.833160147 2 3.789398611 2.964243654 2.986802005 3.277216432 6.822349843 3 5.009270252 4.830641381 4.449611046 4.974840546 7.379708406
8
K-Means with 4 clusters
15:22 Tuesday , September 12, 2006 Replace=FULL The FASTCLUS Procedu re Radius=0 Maxc lus t e r s =4 Maxi t e r = 1 In i t i a l Seeds 43
Clus t e r ct cx rd vi ra 1 4.00000000 6.00000000 20.00000000 30.00000000 55.00000000 2 28.00000000 27.00000000 22.00000000 30.00000000 77.00000000 3 28.00000000 27.00000000 32.00000000 27.00000000 29.00000000 4 4.00000000 6.00000000 4.00000000 8.00000000 16.00000000 Cr i t e r i o n Based on Fina l Clus t e r Seeds = 4.4230
Summary
Maximum Dis t ance RMS Std f r om Seed Radius Neares t Dis t ance Between Clus t e r Frequency Dev ia t i o n to Observa t i o n Exceeded Clus t e r Clus t e r Cent r o i d s 1 110 3.7629 20.7175 3 14.1673 2 96 4.1853 20.1725 1 17.4524 3 73 4.0331 18.6470 1 14.1673 4 37 4.9717 20.1480 3 19.3928 Sta t i s t i c s fo r Var i ab l e s
Var i ab l e Tota l STD With i n STD R- Square RSQ/(1 - RSQ) ct 6.44124 4.41131 0.535443 1.152587
9
cx rd vi ra OVER-ALL
3.86216 3.45478 3.68555 16.20770 8.30198
3.74859 3.24522 3.56847 5.25860 4.10925 =
0.066918 0.126040 0.071454 0.895734 0.757335 324.57 0.73594
0.071717 0.144217 0.076952 8.590883 3.120910
Pseudo F Sta t i s t i c Approx ima t e Expec ted Over - Al l Cubic Clus t e r i n g
R- Squared = = fo r 2.166
Cr i t e r i o n
WARNING: The two va l ues above are i n va l i d
cor r e l a t e d var i a b l e s .
10
15:22 Tuesday , September 12, 2006 Replace=FULL The FASTCLUS Procedu re Radius=0 Maxc lus t e r s =4 Maxi t e r = 1 Clus t e r Means
44
Clus t e r ct cx rd vi ra 1 17.51818182 23.51818182 18.46363636 25.96363636 47.70909091 2 23.81250000 25.61458333 21.11458333 28.23958333 63.46875000 3 14.80821918 25.24657534 19.23287671 27.17808219 33.98630137 4 9.00000000 23.35135135 17.81081081 25.97297297 15.67567568 Clus t e r Standa rd Dev ia t i o n s
Clus t e r ct cx rd vi ra 1 4.853319715 3.595950368 2.888373634 2.967491365 4.142740870 2 3.776136985 2.988996634 2.926718269 3.224069973 6.740221963 3 4.566355021 3.612404639 3.061955384 3.392567308 5.151842040 4 4.242640687 5.740771416 4.965647758 5.766216199 3.837346117
11
Frequency Table and Scatter Plot with 3 Clusters
15:22 Tuesday , September 12, 2006 The FREQ Procedu re Tab le of d9 by CLUSTER d9(d9 ) CLUSTER(Clus t e r ) 55
Frequency , Percen t , Row Pct , Col Pct , 1, 2, 3, ^ ^ ^ ^ N , 13 , 2 , 12 , , 4.11 , 0.63 , 3.80 , , 48.15 , 7.41 , 44.44 , , 8.72 , 1.96 , 18.46 , ^ ^ ^ ^ Y , 136 , 100 , 53 , , 43.04 , 31.65 , 16.77 , , 47.06 , 34.60 , 18.34 , , 91.28 , 98.04 , 81.54 , ^ ^ ^ ^ Tota l 149 102 65 47.15 32.28 20.57
Tota l 27 8.54
289 91.46
316 100 .0
12
Can2 4
3
2
1
0
- 1
- 2
- 3
- 4
- 5
- 6 - 6 - 5 - 4 - 3 - 2 - 1 0 Can1 Cl us t er 1 2 3 1 2 3 4 5 6
13
Hierarchical Clustering
SAS Program
PROC IMPORT OUT= WORK.DATA1 DATAFILE= "C:\Documents and Settings\rhightower\My Documents\Teaching\BIAnalysis\Gadgets.xls" DBMS=EXCEL2002 REPLACE; GETNAMES=YES; Run; data data2; set data1; /* ct cx rd vi ra */ compatibility complexity result demonstrability visibility relative advantage
ct=ct1+ct2+ct3+ct4; cx=cx1+cx2+cx3+cx4+cx5+cx6; rd=rd1+rd2+rd3+rd4; vi=vi1+vi2+vi3+vi4+vi5+vi6; ra=ra1+ra2+ra3+ra4+ra5+ra6+ra7+ra8+ra9+ra10+ra11; ui=ui1+ui2+ui3+ui4+ui5+ui6; if ct=. or cx=. or rd=. or vi=. or ra=. then delete; run; /* method=single linkage outtree outputs the results so the dendogram can be drawn ccc asks for the cubic clustering criterion pseudo asks for the pseudo F and tsquared tests print=15 requests only the last 15 generations of cluster history be printed */
14
proc cluster method=sin outtree=mictree ccc pseudo print=15; var ct cx rd vi ra ; run; /* proc tree draws the dendogram and uses the output from proc cluster the results are output so we can examine the clusters nclusters indicates how many clusters we're choosing */ proc tree data=mictree out=New nclusters=3 horizontal; copy ct cx rd vi ra ; run; /* */ Sort the data by cluster and then print the means by cluster
proc sort;by cluster;run; proc means;var ct cx rd vi ra ;by cluster; run; /* proc gplot draws the scatter diagram and uses a different symbol for each cluster */ proc gplot symbol1 symbol2 symbol3 ; v=star c=red; v=diamond c=green; v=plus c=black;
plot ra*ct=cluster; run;
15
Single Linkage Method
15:22 Tuesday , September 12, 2006 The CLUSTER Procedu re Sing l e L inkage Clus t e r Ana lys i s Eigenva l ue s of the Covar i a n ce Mat r i x Eigenva l ue 1 2 3 4 5 288.853743 23.544625 13.894729 10.759946 7.561032 Di f f e r e n ce 265.309118 9.649896 3.134783 3.198915 Propor t i o n 0.8382 0.0683 0.0403 0.0312 0.0219 Cumula t i v e 0.8382 0.9065 0.9468 0.9781 1.0000 = 8.301977 = 22.7678 56
Root - Mean- Square Tota l - Sample Standa rd Devia t i o n Mean Dis t ance Between Observa t i o n s Clus t e r NCL 15 14 13 12 11 10 9 8 7 6 5 4 3 - - Clus t e r s CL17 CL15 CL14 CL13 CL12 CL11 CL10 CL9 CL8 CL7 CL6 CL5 CL4 Jo i ned - OB62 OB224 CL27 CL16 CL18 OB102 OB309 OB242 OB89 OB262 OB148 OB226 OB166 FREQ 299 300 302 304 306 307 308 309 310 311 312 313 314 SPRSQ 0.0030 0.0019 0.0061 0.0139 0.0047 0.0022 0.0038 0.0051 0.0140 0.0133 0.0068 0.0045 0.0162 His t o r y RSQ .117 .115 .109 .095 .090 .088 .084 .079 .065 .052 .047 .041 .024 ERSQ .893 .889 .885 .881 .875 .869 .862 .854 .845 .832 .815 .791 .748
CCC - 61 - 60 - 60 - 59 - 59 - 58 - 57 - 48 - 48 - 47 - 37 - 36 - 24
PSF 2.8 3.0 3.1 2.9 3.0 3.3 3.5 3.8 3.6 3.4 3.9 4.4 3.9
PST2 1.0 0.7 2.1 4.7 1.6 0.7 1.3 1.7 4.7 4.4 1.5 2.2 5.3
Norm Min Dis t 0.3727 0.3904 0.3928 0.4049 0.4144 0.4303 0.4607 0.5046 0.5215 0.571 0.6102 0.6196 0.6304
T i e
16
2 1
CL3 CL2
OB88 OB269
315 316
0.0059 0.0185
.019 .000
.630 .000
- 21 0.00
5.9 .
1.9 5.9
0.6529 0.6529
T
15:22 Tuesday , September 12, 2006
57
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CLUSTER=1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - The MEANS Procedu re Var i ab l e N Mean Std Dev Min imum Maximum ct 314 17.8949045 6.3662277 4.0000000 28.0000000 cx 314 24.6528662 3.5780492 12.0000000 39.0000000 rd 314 19.4171975 3.3546245 4.0000000 32.0000000 vi 314 26.9872611 3.5343811 12.0000000 38.0000000 ra 314 45.6401274 16.1643253 11.0000000 77.0000000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CLUSTER=2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Var i ab l e N Mean Std Dev Min imum Maximum ct 1 4.0000000 . 4.0000000 4.0000000 cx 1 6.0000000 . 6.0000000 6.0000000 rd 1 20.0000000 . 20.0000000 20.0000000 vi 1 30.0000000 . 30.0000000 30.0000000 ra 1 55.0000000 . 55.0000000 55.0000000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CLUSTER=3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Var i ab l e N Mean Std Dev Min imum Maximum ct 1 4.0000000 . 4.0000000 4.0000000 cx 1 6.0000000 . 6.0000000 6.0000000 rd 1 4.0000000 . 4.0000000 4.0000000 vi 1 8.0000000 . 8.0000000 8.0000000
17
ra 1 16.0000000 . 16.0000000 16.0000000
18
r a 80
70
60
50
40
30
20
10 0 10 ct CL US T E R 1 2 3 20 30
19
Complete Linkage Method
15:22 Tuesday , September 12, 2006 The CLUSTER Procedu re Comple t e L inkage Clus t e r Ana lys i s Eigenva l ue s of the Covar i a n ce Mat r i x Eigenva l ue 1 2 3 4 5 288.853743 23.544625 13.894729 10.759946 7.561032 Di f f e r e n ce 265.309118 9.649896 3.134783 3.198915 Propor t i o n 0.8382 0.0683 0.0403 0.0312 0.0219 Cumula t i v e 0.8382 0.9065 0.9468 0.9781 1.0000 = 8.301977 = 22.7678 60
Root - Mean- Square Tota l - Sample Standa rd Devia t i o n Mean Dis t ance Between Observa t i o n s Clus t e r NCL 15 14 13 12 11 10 9 8 7 6 5 4 3 - - Clus t e r s CL21 OB88 CL27 CL16 CL19 CL17 CL11 CL13 CL12 CL8 CL10 CL9 CL5 Jo i ned - CL62 CL40 CL23 CL48 CL18 CL29 CL24 OB226 CL22 CL15 CL14 CL35 CL7 FREQ 57 3 78 42 36 91 39 79 45 136 94 41 139 SPRSQ 0.0132 0.0028 0.0083 0.0030 0.0049 0.0058 0.0048 0.0042 0.0070 0.0583 0.0077 0.0108 0.0420 His t o r y RSQ .852 .849 .840 .837 .833 .827 .822 .818 .811 .753 .745 .734 .692 ERSQ .893 .889 .885 .881 .875 .869 .862 .854 .845 .832 .815 .791 .748
CCC - 9.5 - 9.0 - 9.6 - 9.0 - 8.7 - 8.4 - 7.7 - 5.9 - 5.2 - 11 - 7.3 - 5.6 - 3.6
PSF 123 130 133 142 152 162 177 197 221 189 227 287 352
PST2 39.8 3.1 19.5 5.8 7.8 14.2 6.2 7.9 11.6 102 15.5 12.3 66.6
Norm Max Dis t 0.9722 0.988 1.0016 1.0366 1.0705 1.1546 1.2508 1.26 1.2858 1.4313 1.4421 1.6428 1.8871
T i e
20
2 1
CL3 CL2
CL4 CL6
180 316
0.1743 0.5178
.518 .000
.630 .000
- 5.8 0.00
337 .
179 337
2.3386 3.2709 61
15:22 Tuesday , September 12, 2006
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CLUSTER=1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - The MEANS Procedu re Var i ab l e N Mean Std Dev Min imum Maximum ct 136 22.6397059 4.2733850 10.0000000 28.0000000 cx 136 24.9191176 2.9440590 12.0000000 33.0000000 rd 136 20.7132353 2.9158776 10.0000000 28.0000000 vi 136 27.5367647 3.2521402 18.0000000 38.0000000 ra 136 60.0588235 7.8822489 46.0000000 77.0000000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CLUSTER=2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Var i ab l e N Mean Std Dev Min imum Maximum ct 139 15.6690647 4.6052719 4.0000000 28.0000000 cx 139 24.4964029 4.0457064 6.0000000 39.0000000 rd 139 18.4244604 3.0023624 4.0000000 32.0000000 vi 139 26.5035971 3.2845137 18.0000000 34.0000000 ra 139 39.9856115 6.5981833 25.0000000 55.0000000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CLUSTER=3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Var i ab l e N Mean Std Dev Min imum Maximum ct 41 9.0243902 4.1921820 4.0000000 18.0000000 cx 41 23.3902439 5.4675317 6.0000000 36.0000000 rd 41 18.1219512 4.8331932 4.0000000 28.0000000 vi 41 26.4146341 5.6479005 8.0000000 33.0000000 ra 41 16.4878049 4.4447832 11.0000000 26.0000000
21
22
r a 80
70
60
50
40
30
20
10 0 10 ct CL US T E R 1 2 3 20 30
23
Ward's Method
15:22 Tuesday , September 12, 2006 The CLUSTER Procedu re Ward ' s Min imum Var i an ce Clus t e r Ana lys i s 62
Eigenva l ue s of the Covar i a n ce Mat r i x Eigenva l ue 1 2 3 4 5 288.853743 23.544625 13.894729 10.759946 7.561032 Di f f e r e n ce 265.309118 9.649896 3.134783 3.198915 Propor t i o n 0.8382 0.0683 0.0403 0.0312 0.0219 Cumula t i v e 0.8382 0.9065 0.9468 0.9781 1.0000 = 8.301977 = 26.25316
Root - Mean- Square Tota l - Sample Standa rd Devia t i o n Root - Mean- Square Dis t ance Between Observa t i o n s Clus t e r NCL 15 14 13 12 11 10 9 8 7 6 5 4 3 - - Clus t e r s CL39 CL24 CL22 CL20 CL28 CL34 CL13 CL18 CL12 CL11 CL8 CL9 CL10 Jo i ned - - CL25 CL47 CL19 CL49 CL21 CL16 CL23 CL15 CL27 CL14 CL54 CL7 CL6 FREQ 36 11 72 41 29 48 77 65 71 40 80 148 88 His t o r y RSQ .876 .872 .867 .862 .856 .848 .840 .831 .820 .808 .786 .751 .694 ERSQ .893 .889 .885 .881 .875 .869 .862 .854 .845 .832 .815 .791 .748
SPRSQ 0.0043 0.0046 0.0048 0.0050 0.0057 0.0082 0.0084 0.0086 0.0107 0.0127 0.0215 0.0354 0.0568
CCC - 4.2 - 4.3 - 4.3 - 4.2 - 4.2 - 4.5 - 4.6 - 3.9 - 3.8 - 3.7 - 3.3 - 4.1 - 3.4
PSF 152 158 165 173 182 190 201 217 235 260 286 313 355
PST2 13.1 4.5 15.9 15.2 12.7 14.3 20.5 20.2 28.4 15.3 44.6 68.3 61.9
T i e
24
2 1
CL4 CL2
CL5 CL3
228 316
0.1725 0.5216
.522 .000
.630 .000
- 5.6 0.00
342 .
229 342
15:22 Tuesday , September 12, 2006
63
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CLUSTER=1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - The MEANS Procedu re Var i ab l e N Mean Std Dev Min imum Maximum ct 80 24.4000000 3.3549510 16.0000000 28.0000000 cx 80 25.7500000 3.1842174 12.0000000 33.0000000 rd 80 21.5625000 2.7597640 10.0000000 28.0000000 vi 80 28.6875000 3.0339586 18.0000000 38.0000000 ra 80 64.7625000 6.5588741 53.0000000 77.0000000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CLUSTER=2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Var i ab l e N Mean Std Dev Min imum Maximum ct 148 17.6891892 4.7133496 4.0000000 28.0000000 cx 148 23.8986486 3.2799947 6.0000000 31.0000000 rd 148 18.6689189 2.7857473 4.0000000 27.0000000 vi 148 26.0675676 2.9615729 18.0000000 33.0000000 ra 148 47.5337838 5.7147804 36.0000000 61.0000000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CLUSTER=3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Var i ab l e N Mean Std Dev Min imum Maximum ct 88 12.0113636 5.3379886 4.0000000 28.0000000 cx 88 24.5000000 4.9572888 6.0000000 39.0000000 rd 88 18.5568182 4.1546483 4.0000000 32.0000000 vi 88 26.8068182 4.6777978 8.0000000 34.0000000
25
ra 88 24.8409091 8.7728504 11.0000000 38.0000000
26
N a me
of
Ob s e r v a t i o n o r
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Berkeley - WEEK - 1
How many genes? Mapping mouse traitsLecture 1, Statistics 246 January 20, 20041Aim of todays and Thursdays lectureTo review basic Mendelian genetics, the basics of recombination, and go on to see how genes contributing to qualitative and quanti
Berkeley - PHYSICS - 7
Known Values Me (earth mass) = 5.97 1024 kg Mm (moon mass) = 7.36 1022 kg Re (earth radius) = 6.37 106 m Rm (moon radius) = 1.74 106 mx (spring compression) = 1000m D (me distance) =3.85 107 m m (projectile mass) = 1 kg G = 6.67 1011 m3
UCF - CDA - 6938
Modeling, Early Detection, and Mitigation of Internet Worm AttacksCliff C. ZouAssistant professor School of Computer Science University of Central Florida Orlando, FL Email: czou@cs.ucf.edu Web: http:/www.cs.ucf.edu/~czou1Worm propagation proces
Berkeley - CS - 265
Runtime Optimization with SpecializationJohnathon Jamison CS265 Susan Graham 4-30-2003What is Runtime Code Generation (RTCG)? Dynamic addition of code to the instruction stream Restricted to instructions executed directly by hardwareProblems w
Berkeley - CS - 268
CS 268: Computer NetworkingL-6 Routers and End-to-End Congestion ManagementTCP & Routers RED XCP Assigned reading [FJ93] Random Early Detection Gateways for Congestion Avoidance [KHR02] Congestion Control for High Bandwidth-Delay Product Net
Berkeley - CS - 268
CS 268: Computer NetworkingL-10 Wireless in the Real WorldWireless in the Real World Real world deployment patterns Mesh networks and deployments Assigned reading Modeling Wireless Links Architecture and Evaluation of an Unplanned 802.11b Me
Berkeley - RETREAT - 1
GDI Environmental monitoring app Data & lessons learnedRobert Szewczyk Joe Polastre Alan Mainwaring David CullerJanuary 15, 2002Outline Application overview Sensor node analysis Network analysis ConclusionsGreat Duck Island Petrel monitor
Berkeley - RETREAT - 6
More routing protocolsAlec Woo June 18th, 2002Motivation Empirical data shows a probabilistic model of packet reception over distance Long links Asymmetric links Packet Reception Probability 100% distance A routing tree using poor link "disco
Berkeley - RETREAT - 1
MultHop Routing:just say noKevin Fall*Enologically enhancedMay not represent the opinion of Intel CorpMulti Hop Routing Great research Topic Like IP Multicast and QoS/CoS At least a decade of PhD theses You [almost] never really need it D
Berkeley - RETREAT - 1
Mica Weather BoardMicroWeatherStationJosephPolastreUniversityofCalifornia,Berkeleypolastre@cs.berkeley.eduDesignGoals Sense events relevant to scientists Calibrated sensor with meaningful units Miniature size to prevent disturbing existing ha
Berkeley - RETREAT - 1
Ivy Equipment Inventory SystemJaein Jeong Barbara Hohlt Kris PisterMotivation Research centers have a number of shared equipment and they moving around. Equipment tracking can help better utilize the equipment. With wireless sensor nodes, the e
Berkeley - RETREAT - 1
TOSSIM: Visualizing the Real WorldPhilip Levis, Nelson Lee, Dennis Chi and David Culler UC BerkeleyNEST Retreat, January 2003The Problem Your TinyOS application doesnt work Is the network so messy that routing fails? Is there a bug in your rou
Berkeley - LS - 107
LS 107 Course Notes (Prof. Kutz) Nozick on rights and entitlements (Feb. 14-21) General theme for Nozick p. ix: Individuals have rights, and there are things no person or group may do to them (without violating their rights). So strong and far-reachi
Berkeley - SCOPSEQ - 1
Changes and additions from ASTRAL 1.55 to 1.57:* Default sequence setGenetic domain sequences, first introduced in 1.55, are now thedefault sequence set in ASTRAL. Original-style sequences are also available.* Single sequence retrievalFASTA
Berkeley - ENGIN - 110
Ti t l e: Cr eat or : Apps of t Dr aw Cr eat i onDat e: Thu M ar 16 08: 40: 05 1995MultiLink Distributed Solutions5. Sales PlanMultiLink's sales plan has two phases. Phase I includes marketing Software Fault Tolerance to large corporations and s
Berkeley - BIO - 1
Bio1bSummer2008 EricHarrisEcologyLecture3 Page1of2ECOLOGYLECTURE3:POPULATIONBIOLOGYIDEMOGRAPHY&LIFEHISTORY Reading:7thed.,11361143;8thed.,11741181. Apopulation=agroupofspeciesofthesamespeciesinthesamegeneralarea(recall: populationgenetics) A.Popu
Berkeley - RETREAT - 02
CQual:A Tool for Adding Type Qualifiers to CJeff Foster et al UC Berkeley OSQ Retreat, May 21-23 2002Background oftwareis buggy! S provethequality of software ? How can weim Wewant to build tools to analyzesourcecode pile e Find bugs at com
UCF - ETG - 4950
UNIVERSITY OF CENTRAL FLORIDA Department of Engineering Technology GeneralGuidelines, Rules and Procedures for the Senior Design Project ETG4950C (3 credits)Prepared by: Dr. A. Rahrooh, BSEET - Advisor and Program Coordinator Modified by: Dr. Al Du
UCF - ETI - 3671
UNIVERSITY OF CENTRAL FLORIDA DEPARTMENT OF ENGINEERING TECHNOLOGY COURSE OUTLINE SPRING2004Title and Course Number:ETI 3671Technical Economic AnalysisHours 2 (2, 0) Course Description:Analysis of cost elements in technical operations. Basis
UCF - ETG - 3541
UNIVERSITY OF CENTRAL FLORIDA DEPARTMENT OF ENGINEERING TECHNOLOGY Spring-2004 ASSIGNMENT SHEET / SYLLABUS ETG-3541-01 APPLIED MECHANICS January 5, 2004 Instructor: Dr. N. MisconiOffice Hours: Tue, Thurs, 15:00-16:30 Orlando Office Rm. #218 KSC Off
UCF - ETM - 4220
UNIVERSITY OF CENTRAL FLORIDA DEPARTMENT OF ENGINEERING TECHNOLOGY ASSIGNMENT SHEET / SYLLABUS SPRING 2004ETM- 4220 APPLIED ENERGY SYSTEMS January 5, 2004 INSTRUCTOR: Dr. N. MisconiOffice:Orlando Campus, Engineering Building 1 Rm. #218 Phone (4
Berkeley - I - 15
Content AnalysisProf. Marti Hearst SIMS 202, Lecture 15SIMS 202, Marti HearstReview ssContent Analysis: s Transformation of raw text into more computationally useful forms Words in text collections exhibit interesting statistical propertie
Berkeley - I - 202
Content AnalysisProf. Marti Hearst SIMS 202, Lecture 15SIMS 202, Marti HearstReview ssContent Analysis: s Transformation of raw text into more computationally useful forms Words in text collections exhibit interesting statistical propertie
Berkeley - I - 202
Lecture 02: Info/History/PhotoSIMS 202: Information Organization and RetrievalProf. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30 am - 12:00 am Fall 2002IS 202 - Fall 2002 2002.08.29 - SLIDE 1Lecture Outline What Is
Berkeley - I - 202
Tips on how to make ER-Diagrams in PowerPoint:Use the Drawing Toolbar.If the Drawing Toolbar isn't visible. Under the Tools menu chose Customize. Click the Toolbars tab to make it active. Check the box for Drawing. Click on Close. On the Drawing T
UCF - NE - 787013
Neil HamiltonLab Section: 0029 Major: Management Information SystemsManagement Information Systems is a major that will allow me to combine my interests in computer systems and business. The job opportunities for people in this field are boundless
Berkeley - EE - 290
Switched Capacitor Circuits for DC-DC ConversionChi Law Matthew Senesky Nov. 25, 20031Motivation Pro No magnetic elements Possible IC implementation Con Control difficult Lower power applications More info in Bill and Eddie's talk2Sw
Berkeley - EE - 141
UNIVERSITY OF CALIFORNIA College of Engineering Department of Electrical Engineering and Computer SciencesLast modified on April 21, 2006 by Seng Oon Toh (sengoon@eecs)Borivoje Nikoli EE 141Homework #9: Activity Factor and Sequential CircuitsP
Berkeley - BEE - 2
iBob TutorialDejan Markovic, Zhengya Zhang {dejan, zhengya}@eecs.berkeley.edu1The iBob Xilinx (Virtex2p) emulation boardIO5V DC2Step 1: SysGen SetupXSG Output: *.prj Part as specified in the screen shot Synthesis tool FPGA clock (
Berkeley - B - 6
<!DOCTYPE html PUBLIC "-/W3C/DTD XHTML 1.0 Strict/EN" "http:/www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http:/www.w3.org/1999/xhtml"> <head> <title> /code/b6lowpan/branches/hc/apps/IPBaseStation/README.txt (log)
Berkeley - B - 6
<!DOCTYPE html PUBLIC "-/W3C/DTD XHTML 1.0 Strict/EN" "http:/www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http:/www.w3.org/1999/xhtml"> <head> <title> /code/b6lowpan/branches/hc_centralized/apps/UDPEcho/sim/15-15-
Berkeley - B - 6
<!DOCTYPE html PUBLIC "-/W3C/DTD XHTML 1.0 Strict/EN" "http:/www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http:/www.w3.org/1999/xhtml"> <head> <title> /code/b6lowpan/branches/hc_centralized/apps/UDPEcho/sim/meyer-
Berkeley - B - 6
<!DOCTYPE html PUBLIC "-/W3C/DTD XHTML 1.0 Strict/EN" "http:/www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http:/www.w3.org/1999/xhtml"> <head> <title> /code/b6lowpan/branches/hc_centralized/apps/UDPEcho/sim/small.
Berkeley - B - 6
<!DOCTYPE html PUBLIC "-/W3C/DTD XHTML 1.0 Strict/EN" "http:/www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http:/www.w3.org/1999/xhtml"> <head> <title> /code/b6lowpan/tags/release-7-15-2008/apps/IPBaseStation/READM
Berkeley - B - 7
<!DOCTYPE html PUBLIC "-/W3C/DTD XHTML 1.0 Strict/EN" "http:/www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http:/www.w3.org/1999/xhtml"> <head> <title> /code/b6lowpan/tags/release-7-15-2008/apps/IPBaseStation/READM
Berkeley - B - 6
<!DOCTYPE html PUBLIC "-/W3C/DTD XHTML 1.0 Strict/EN" "http:/www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http:/www.w3.org/1999/xhtml"> <head> <title> /code/b6lowpan/tags/release-8-25-2008/apps/UDPEcho/NodeConnect
Berkeley - B - 8
<!DOCTYPE html PUBLIC "-/W3C/DTD XHTML 1.0 Strict/EN" "http:/www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http:/www.w3.org/1999/xhtml"> <head> <title> /code/b6lowpan/tags/release-8-25-2008/apps/UDPEcho/NodeConnect
Berkeley - DECEMBER - 2002
California Welfare-to-Work Transportation Needs Assessment StudyEvelyn Blumenberg UCLA Lewis Center for Regional Policy StudiesOctober 9, 2002Carrie's Morning Well.my typical day is I get up at 5:00 am. I get my four daughters ready for school,
UCF - MAR - 3023
Chapter 9 Product ConceptsWhat's a Product? Good Service IdeaExperience CredenceProduct is the starting point of Marketing Mix ProductPricePromotionPlace (Distribution)Product-Service ContinuumAuto with repair Restaura Airline tr
UCF - TAX - 6405
University of Central Florida Dixon School of Accounting Tax 6405Estate Tax Text: Chapters 18-201Outline Section 2036 Retained life estate Section 2038 Revocable transfers Section 2037 Transfers taking effect at death2Section 2036(a)
Berkeley - ECON - 102
Designing Institutions for Sustainable Water UseProf. David Sunding UC Berkeley March 10, 2005Population Trends and Water Use Global population has grown from 1 billion in 1800 to 2.5 billion in 1950 to 6 billion in 2000 to ? Along with populati
UCF - COMMONDATA - 00
Common Data Set 2000-2001Common Data Set Definitions 2000 All definitions related to the financial aid section appear at the end of the Definitions document. Items preceded by an asterisk (*) represent definitions agreed to among publishers which
UCF - COMMONDATA - 99
Common Data Set: 1999-2000 FINALCommon Data Set Definitions 1999 All definitions related to the financial aid section appear at the end of the Definitions document. Items preceded by an asterisk (*) represent definitions agreed to among publishers
UCF - CH - 108927
Chaban, 1TheLifeofTomSiebelTom Siebel attended the University of Illinois at Urbana-Champaign. He graduated with a bachelor in History and a master in computer science. In 1984 he joined Oracle and in the first year he sold 280 percent of his sale
Berkeley - CS - 268
CS 268: Computer NetworkingL-16 P2POverview P2P Lookup Overview Centralized/Flooded Lookups Routed Lookups Chord Comparison of DHTs2Peer-to-Peer Networks Typically each member stores/provides access to content Has quickly grown in popu
UCF - ER - 092752
Holland, page 1Palm Makes it PossibleThe article "Ubiquitous Palm Is a Phenomenon," on page 5.14 in Discovering Computers 2003, briefly introduces on of the most important companies in the computer field that's leading the way in handheld compute
UCF - FIN - 4453
Chapter 8 Common Stock ValuationValue How much an asset is worth The amount that a willing and able buyeragrees to pay for an asset to a willing and able sellerWhat is Value? Book Value the price of an asset minus itsaccumulated deprecia
UCF - FIN - 4453
Chapter 12 Risk & DiversificationRisk Possibility of a loss Difficult concept to define Focus on possibility of a financial loss The larger the possibility of loss, the largerthe riskSome Statistical Concepts Discrete Distribution vs. Cont
UCF - FIN - 4453
Chapter 11: Example The Supreme Shoe Company is considering the purchase of a new, fully automated machine to replace a manually operated one. The machine being replaced, now five years old, originally had an expected life of 10 years, is being depre
UCF - COT - 4210
COT 4210Section CSpring 2001Theorem 2 established that NFAs are no more "powerful" than DFAs in terms of the family of languages they can recognize. However, NFAs are more compact and succinct than DFAs because of the exponential relationship b
UCF - COT - 4210
COT 4210 The Minimal State DFASection BXSummer 2006We have characterized Regular languages in terms of NFAs (DFAs) and RLGs (LLGs). Throughout our discussions we have continually drawn an analogy between grammars and programs and DFAs and progr
UCF - COT - 4810
COT 4810 Homework #1 (9/9-9/11), due 9/18 in class Finite Automata (Cht. 2) 1) Construct a DFA M over the alphabet {a,b} that accepts all strings that contain the substring bb. There are many solutions. Here's one: Q = {q1, q2, q3} Start State = q1 A
UCF - COT - 5405
Design & Analysis of Algorithms COT 5405 Instructor: Dr. Arup GuhaNote-Takers: Ramya Tummala, Shamik Sengupta Date: 09/09/03L={0 2 } , Where n is a non-negative integer. Design a Turing MachineIf there's one `0' ACCEPT. Cross-off every other `0
UCF - MA - 397881
UNITEDSTATESNAVY SEALSBY:MarcosUriostegui Whatdoesittaketobecomea USNAVYSEALMUSTJOINTHEUSNAVY GOTHROUGHBOOTCAMP ATTENDPREBUDS ATTENDBUDS ATTENDPOSTBUDSSCHOOLWHATISPREBUDS(BASIC UNDERWATER DEMOLITION) FIVEDAYINTROCOURSETOBUDS FOLLOWEDBYAT
Berkeley - BUSINESS - 187
BA 187 International TradeKrugman & Obstfeld, Chapter 8 Instruments of Trade Policy1Free enterprise made this country. Free trade will destroy it. For five years, Ive been advocating a 20 percent tariff on all imports. We can either do that, or
UCF - COP - 3330
COP 3330 Final Exam Review DATE: April 26. 2007 (THURSDAY) TIME: 4 7 PM PLACE: CSB 101I. The Basics (Chapters 2, 5, 6) a. comments b. identifiers, reserved words c. white space d. compilers vs. interpreters e. syntax, semantics f. errors i. syntax
Berkeley - EPS - 109
Simple Erosion ModelMade by Jeffrey SmithAims to recreate terrain we see on Earth based only on the simple principal that rain falls, flows downhill, and causes erosion. The following logic was used in this simulation: 1. 2. 1. 1. Start with a ma
UCF - FIN - 4453
CHAPTER 10CAPITAL BUDGETINGCAPITAL BUDGETING Estimating the Cash Flows Decision Criteria Sensitivity Analysis Optimal Capital BudgetESTIMATING CASH FLOWS Important considerations in cash flows analysisa) Incremental: b) After-Tax: Cash flo
Berkeley - ASTRO - 12
The Outer Planets Planet 5 Jupiter 1. Like the rest of the outer planets, Jupiter has no surface. How is that possible?2. What two elements is Jupiter primarily composed of?3. What is the cause of Jupiter's banded cloud patterns?4. What is the
Berkeley - EE - 126
here are the answers to some generalquestions about the grades:1. homework grades will be given base on not only on the correctanswer, but also on your understanding towards the problem.2. For everyone, the lowest homework grade won't be count
Berkeley - LS - 120
Punishment Professor Smith Spring 2006Study topics for Bible readings There are two questions that run through this part of the course: (1) Are the biblical understandings of wrong and punishment different from the modern understandings, and if so