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ISE 535 Data Mining
创建日期：2024-05-20 18:15:10
标签： ISE 535
Data Mining

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ISE 535 Data Mining Exam 1 Due on March 8 by 12:30 pm

For the following questions use the data in the file cities1.xlsx. It contains data on 325 metropolitan cities
in the United States.
• Let column Metropolitan_Area be the row names of your dataframe.
• Remove the non-numeric variables, Crime_Trend and Unemployment_Threat).
• Use scale() function to scale all numeric columns.
• Use function dist to find the distance between cities (on the scaled data).
K-MEANS CLUSTERING
1. (10 pts) Use set.seed(123) and the user function twcv to find TWCV values for k = 1 : 16. Use nstart =
25. Display the elbow chart.
2. (10 pts) The best number of clusters is the smallest k such that the cluster plot shows the least amount
of clusters overlap. Use fviz_cluster( ) with argument geom = ”point” to display cluster plots with
no label names. Try fviz_cluster( ) with different K. What is the best K? For this K find the number
of cities in each cluster.
3. (10 pts) For your choice of K clusters, find the median (or mean, if you prefer) of each numerical column
(on the original un-scaled dataset). Write one sentence characterizing each cluster.
HIERARCHICAL CLUSTERING
4. (20 pts) Use function hclust with linkage ward.D to create object h1 and display the four clusters on the
dendrogram. Use function cuttree( ) to find the clusters. Find the number of cities in each cluster.
Use fviz_cluster( ) with argument geom = ”point” to display the cluster plots of your choice with no
label names. Find the CCPC for ward.D
5. (20 pts) Use function hclust with linkage complete to create object h2 and display the four clusters on
the dendrogram. Use function cuttree( ) to find the clusters. Find the number of cities in each cluster.
Use fviz_cluster( ) with argument geom = ”point” to display the cluster plots of your choice with no
label names. Find the CCPC for complete linkage.
6. (20 pts) Use function hclust with linkage average to create object h3 and display the four clusters on the
dendrogram. Use function cuttree( ) to find the clusters. Find the number of cities in each cluster.
Use fviz_cluster( ) with argument geom = ”point” to display the cluster plots of your choice with no
label names. Find the CCPC for average linkage.
7. (10 pts) What linkage do you prefer? For the clusters found for this linkage find the median (or mean, if
you prefer) of each numerical column (on the original un-scaled dataset). Write one sentence characterizing
each cluster for this linkage.
Submit your report (code and output) as a pdf file onto Blackboard (no screen captures). Read your pdf file
before submitting.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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