Clustering

The purpose of this assignment is to perform cluster analysis and
analyze clusters in a data set to determine whether or not the
information generated can be used to address a specific business
problem.For this assignment, you will use the “Wholesale Customers” data set
from the Topic Materials. Most data categories are self-explanatory.
Clarifying notes are as follows.Fresh: Annual spending (in $1000s) on fresh productsMilk: Annual spending (in $1000s) on milk productsGrocery: Annual spending (in $1000s) on grocery productsFrozen: Annual spending (in $1000s) on frozen productsDetergents_Paper: Annual spending (in $1000s) on detergents and paper productsDelicatessen: Annual spending (in $1000s) on delicatessen productsClient_Type: Type of client – either HoReCa (Hotel/Restaurant/Café) or RetailRegion: Region of client – either Lisbon, Oporto, or OtherA wholesale distributor wants to understand the purchasing profiles
of its clients. If a finite set of distinct profiles were defined, then a
marketing strategy could be designed specifically for each set of
similar clients. The company has compiled a data set that includes
annual client spending on diverse product categories. You have been
tasked with analyzing the data to determine what patterns emerge and how
these patterns can be used to create specific client marketing
profiles.Use k-means clustering to explore and analyze the data set by using only the quantitative variables to cluster the clients.Question 1: Explain the process you used to
define the clusters such as the number of clusters formed, the specific
variables used, etc. Include the “Cluster Sizes” and “Predictor
Importance” outputs when submitting the answer.Question 2: Interpret the clusters with
respect to the quantitative variables that were used in forming the
clusters. Include the “Clusters” output when submitting the answer.Question 3: Discuss whether there is a
pattern in the clusters with respect to the qualitative variables (i.e.,
Client_Type or Region). Include the charts illustrating these patterns
when submitting the answer.Question 4: Provide an appropriate name for each cluster using any or all of the variables in the data set.Question 5: Based upon your analysis, what
patterns emerged and how can these patterns be used to create specific
client marketing profiles? Include discussion of the characteristics for
each profile. Present your finding in the form of a 250-word executive
summary that includes relevant data, charts, and tables.General Requirements:Submit the answers to Questions 1-4 and the executive summary as Word documents.APA format is not required, but solid academic writing is expected.This assignment uses a grading rubric. Please review the rubric prior
to beginning the assignment to become familiar with the expectations
for successful completion.
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mis_655_rs_wholesale_customers.xlsx

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3383
375
31276
1917
4469
9408
2381
26373 36423
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5154
4337
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12205
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1640
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219
10362
20874
11867
16117
22925
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7864
24904
11405
12754
9198
14069
54259
6152
21412
1095
4051
3916
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1449
3683
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1970
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6245
11601
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7260
2820
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1266
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9540
9232
1563
3327
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73498
5025
542
3836
596
2762
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21955
55571
10868
28921
1980
6996
5876
11532
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5005
26866
10487
1648
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6544
15775
3250
45828
7417
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3202
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3
6532
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3655
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836
169
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Retail
30379
37036
10405
18827
22039
7769
9203
5924
31812
16225
1289
18840
3463
622
1989
3830
17773
2861
355
1725
12434
15177
5531
5224
15615
4822
2926
5809
5414
260
200
955
514
286
2343
45640
12759
11002
3157
12356
112151
694
327
8170
3009
2438
8040
13252
7152
1596
3677
8384
1936
3373
584
1433
1825
3328
1371
9250
55
10690
5291
1366
6570
7704
3651
540
2024
15726
7603
12653
6721
3195
735
717
8675
25862
5479
7677
1208
7845
6958
7330
7075
4888
6036
29627
8533
918
6448
521
8002
7639
5189
8253
1096
1988
34792
2177
2707
542
1651
1765
2022
3135
2368
137
19460
14855
2474
9618
14682
12822
283
3810
26870
8584
19858
9170
3268
803
2155
13430
19816
6536
19805
5241
11874
6536
4533
4945
2500
8887
18148
10518
4710
1139
854
9819
11687
321
2995
8425
118
42
926
1286
4052
800
853
531
3001
779
75
233
317
3378
930
398
824
1092
2665
2367
2540
4425
993
405
1393
2399
1116
651
333
937
2515
52
7368
1752
1152
4477
402
16745
443
74
2181
3470
6269
2758
51
20
399
516
12591
73
1082
283
113
170
255
352
302
7
11577
6694
811
4004
8077
4424
3
232
13726
3674
7108
4973
1680
79
69
7015
8773
2840
9836
153
4196
1532
20
120
273
1382
4948
6907
334
58
949
3459
6839
188
189
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Lisbon
Lisbon
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Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
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Lisbon
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Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
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Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Lisbon
Retail
HoReCa
HoReCa
HoReCa
Retail
HoReCa
HoReCa
HoReCa
Retail
HoReCa
HoReCa
Retail
Retail
HoReCa
HoReCa
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Retail
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Retail
HoReCa
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Retail
HoReCa
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HoReCa
HoReCa
HoReCa
HoReCa
HoReCa
834
16936
13624
5509
180
7107
17023
30624
2427
11686
9670
3067
4484
25203
583
1956
1107
6373
2541
1537
5550
18567
12119
7291
3317
2362
2806
2532
18044
18
4155
14755
5396
5041
2790
7274
12680
20782
4042
1869
8656
11072
2344
25962
964
15603
1838
11577
6250
295
1461
3485
1012
5139
7209
7097
2154
2280
13240
14399
11487
685
891
11711
780
4737
3748
12729
1895
28326
1012
6602
6551
10765
16599
1475
7504
367
899
7503
1115
2527
659
3243
5921
2204
577
2746
5989
10678
1780
4984
2703
6380
11522
1981
1381
2251
20292
2974
5230
4897
10391
6824
2112
23127
24708
9490
2216
5226
23596
950
6089
5838
16767
1393
39694
2062
6861
11364
15538
36486
2046
15205
1390
1382
10646
2856
5265
1499
4157
9212
1563
572
2501
5615
3828
3838
3316
3833
2824
275
7332
890
547
959
806
7888
18711
1127
3527
520
3941
3549
5065
469
1383
955
878
2946
1859
864
1801
4736
1291
1329
913
1374
179
2532
1285
2306
1765
91
7496
5612
784
660
1759
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950
6845
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638
937
4260
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118
43
187
5618
355
330
763
4314
592
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9959
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284
954
5
9265
288
5316
3381
12420
244
19410
240
3961
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5828
13308
130
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86
56
4167
256
788
70
761
2568
263
4762
694
955
1566
284
409
325
1216
235
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