南航暑期国际课程大数据可视化课程大作业总结与试验报告Word文件下载.docx
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南航暑期国际课程大数据可视化课程大作业总结与试验报告Word文件下载.docx
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Atthesametime,Ilearnedtrainingandtestingdataanddecisiontrees.Inthelecture3,itmainlytalksaboutconfusionmatrixandROCcurve.Aconfusionmatrixisavisualizationtooltypicallyusedinsupervisedlearning,whichcontainsinformationaboutactualandpredictedclassificationsdonebyaclassificationsystem.ROCcurveisanotherwaybesidesconfusionmatricestoexaminetheperformanceofclassifiers.Then,itdoeswithTanagradataminingsoftwareandhowtousethesoftware.
Inthelecture4,ittalksabouttheclassificationofdata,whichhasthreeclassificationmethods,namelylineardiscrimination,neuralnetworkanddecisionstree,asfollow:
Figure3lineardiscrimination
Figure4neuralnetwork
Figure5decisionstree
Amongthem,decisionstreeistheimportantpart,whichdealswithchoosingthesplittingattribute,giniindexandinformationgainandgainratio.Theginiindexofthesplitdataisdefinedas
(3)
Gainratioisamodificationoftheinformationgainthatreducesitsbiasonhigh-branchattributes.Itisdefinedas
(4)
Meanwhile,parallelcoordinatetechniquesandvisualizationarediscussedinthelecture.Wecanrepresentdatain1,2,and3-D,evenin4+dimensions.Thesetwopicturesareexamplesofvisualization:
oneisbadvisualization,theotherisbettervisualization.
Figure6badvisualization
Figure7bettervisualization
Inthelecture5,atfirst,itintroducesvisualdataminingsoftwaretous,namelyLotsofLines.Then,ittalksaboutvisualizationofmultidimensionaldatawithcollocatedpairedcoordinatesandgenerallinecoordinates.
Inthethirdplace,inthelecture6,Ilearnedmachinelearning,includingsupervisedmachinelearningandunsupervisedmachinelearning.AndIlearnedthemethodofdata-visualizationandcomparingvisualization.Then,ittalksaboutclusteringandthemethodofclusteringwhichinvolvesinK-meansclusteringandHierarchicalclustering.IttellsusK-meansclusteringindetail.
Inthelecture7,itintroducescollocatedcoordinatestous.Inthelecture8,itmainlytalksaboutcollaborativelosslessvisualization.Inaddition,itdealswithcollaborativeapproachtoenhancevisualizationandadvantagesoflosslessvisualization.Then,itdemonstratesadvantagesofnewlosslessvisualizationbasedontheideaofcollocatingpairsofthecoordinates.ItstatesapplicationsofCPCstarsanddensepixeldisplays.
Finally,inthelecture9,ittalksaboutenvelopesofvisualdatamining.Inthelecture10,theprofessorgaverationalanswersintermsofquestionsthatstudentsputforward.Throughthecourse,Ihaveapreliminaryknowledgeofdatavisualizationandmining.
experimentalreport
1atwo-classconfusionmatrix
(1)total15datarecords:
n
H
W=x
H-100=y
e=x-y
cm
kg
1
171
74
71
3
2
176
62
76
-14
181
85
81
4
159
50
59
-9
5
167
67
-17
6
158
60
58
7
160
8
170
56
70
9
178
78
10
55
-15
11
177
64
77
-13
12
184
84
-8
13
82
14
-6
15
-5
(2)confusionmatrix
Predicted
Negative
Positive
Actual
(3)computingresults
(1)
FP=
0.222
(2)
FN=
0.167
(3)
TP=
0.833
(4)
TN=
0.778
(5)
AC=
0.800
(6)
P=
0.714
2dataminingbyusingTanagrasoftware
(1)experimentaldata
Outlook
Temp
Humidity
Windy
Class
sunny
75
yes
Play
80
90
DontPlay
no
72
95
69
overcast
83
65
rain
68
96
(2)horizontalaxisisHumidity,verticalaxisisTemp,legendisAttributevalue,attributesareOutlook,WindyandClass.Itsresultsofdataminingarethefollowingfigures.
Figure8itsattributeisOutlook
Figure9itsattributeisWindy
Figure10itsattributeisClass
(3)otherresultsarethefollowingfigures.
Figure11Windy’sdistribution
Figure12Outlook’sdistribution
Figure13regressionscharacteristics
3visualdataminingbyusingLotsofLinessoftware
(2)theresultsofdatamining
Figure14graphicsinfourcoordinates
Figure15graphicswithdatatable
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