You are going to use the travel reviews dataset in UCI's repository, which was created by crawling the travelers' reviews from tripadvisor.com. Reviews on destination across East Asia are considered in 10 categories. Each traveler's rating is categorized as terrible (0), poor (1), Average (2), Very Good (3), or Excellent (4), and average rating is used against each category per user. Use the clustering method to group the destinations that have similar ratings?

Chegg does not let me upload the whole dataset plz answer with the provided dataset.

User ID	Category 1	Category 2	Category 3	Category 4	Category 5	Category 6	Category 7	Category 8	Category 9	Category 10
User 1	0.93	1.8	2.29	0.62	0.8	2.42	3.19	2.79	1.82	2.42
User 2	1.02	2.2	2.66	0.64	1.42	3.18	3.21	2.63	1.86	2.32
User 3	1.22	0.8	0.54	0.53	0.24	1.54	3.18	2.8	1.31	2.5
User 4	0.45	1.8	0.29	0.57	0.46	1.52	3.18	2.96	1.57	2.86
User 5	0.51	1.2	1.18	0.57	1.54	2.02	3.18	2.78	1.18	2.54
User 6	0.99	1.28	0.72	0.27	0.74	1.26	3.17	2.89	1.66	3.66
User 7	0.9	1.36	0.26	0.32	0.86	1.58	3.17	2.66	1.22	3.22
User 8	0.74	1.4	0.22	0.41	0.82	1.5	3.17	2.81	1.54	2.88
User 9	1.12	1.76	1.04	0.64	0.82	2.14	3.18	2.79	1.41	2.54
User 10	0.7	1.36	0.22	0.26	1.5	1.54	3.17	2.82	2.24	3.12
User 11	1.47	1	0.7	0.75	1.66	2.76	3.18	2.89	1.66	2.62
User 12	0.96	2.96	0.29	0.38	0.88	2.08	3.17	2.93	1.66	3.42
User 13	0.74	1.44	2.75	0.45	0.98	1.74	3.2	2.87	1.38	2.34
User 14	0.58	1.64	2.27	0.45	1.26	1.72	3.19	2.91	2.3	2.74
User 15	0.96	1.68	2.29	0.51	1.2	2.84	3.2	2.82	2.02	2.46

Category 1 Category & 2.79 2.63 2.8 2.96 2.78 2.89 2.66 2.81 2.79 1 User ID 2 User 1 3 User 2 4 User 3 5 User 4 6 User 5 7 User 6 6 8 User 7 9 User 8 10 User 9 11 User 10 12 User 11 13 User 12 14 User 13 15 User 14 16 User 15 17 User 16 18 User 17 19 User 18 20 User 19 Category 2 Category 3 Category 4 0.93 1.8 2.29 1.02 2.2 2.66 1.22 0,8 0.54 0.45 1,8 0.29 0.51 1.2 1.18 0.99 1.28 0.72 0.9 1.36 0.26 0.74 1.4 0.22 1.12 1.76 1.04 0.7 1.36 0.22 1.47 1 0.7 0.96 2.96 0.29 0.74 1.44 2.75 0.58 1.64 2.27 1.68 2.29 1.25 2.52 1.76 0.86 1.04 1.76 0.61 1.96 2.49 0.67 1.36 1.36 Category 5 Category 6 Category 7 0.62 0.8 2.42 3.19 0.64 1.42 3.18 3.21 0.53 0.24 1.54 3.18 0.57 0.46 1.52 3.18 0.57 1.54 2.02 3.18 0.27 0.74 1.26 3,17 0.32 0.86 1.58 3.17 0.41 0.82 1.5 3.17 0.64 0.82 2.14 3.18 0.26 1.5 1.54 3.17 0.75 1.66 2.76 3.18 0.38 0.88 2.08 3.17 0.45 0.98 1.74 3.2 0.45 1.26 1.72 3.19 0.51 1.2 2.84 3.2 0.5 1.46 2.08 3.19 0.34 0.06 1.1 3.18 0.66 1.34 1.78 3.2 0.38 0.82 3.38 3.18 Category 9 1.82 1.86 1.31 1.57 1.18 1.66 1.22 1.54 1.41 2.24 1.66 1.66 1.38 2.3 2.02 Category 10 2.42 2.32 2.5 2.86 2.54 3.66 3.22 2.88 2.54 3.12 2.62 3.42 2.34 2.74 2.46 2.32 2.98 2.42 2.8 2.82 2.89 2.93 2.87 2.91 0.96 2.82 2.74 2.73 3.04 2.86 1.41 1.15 1.15 1.79 Category 1 Category & 2.79 2.63 2.8 2.96 2.78 2.89 2.66 2.81 2.79 1 User ID 2 User 1 3 User 2 4 User 3 5 User 4 6 User 5 7 User 6 6 8 User 7 9 User 8 10 User 9 11 User 10 12 User 11 13 User 12 14 User 13 15 User 14 16 User 15 17 User 16 18 User 17 19 User 18 20 User 19 Category 2 Category 3 Category 4 0.93 1.8 2.29 1.02 2.2 2.66 1.22 0,8 0.54 0.45 1,8 0.29 0.51 1.2 1.18 0.99 1.28 0.72 0.9 1.36 0.26 0.74 1.4 0.22 1.12 1.76 1.04 0.7 1.36 0.22 1.47 1 0.7 0.96 2.96 0.29 0.74 1.44 2.75 0.58 1.64 2.27 1.68 2.29 1.25 2.52 1.76 0.86 1.04 1.76 0.61 1.96 2.49 0.67 1.36 1.36 Category 5 Category 6 Category 7 0.62 0.8 2.42 3.19 0.64 1.42 3.18 3.21 0.53 0.24 1.54 3.18 0.57 0.46 1.52 3.18 0.57 1.54 2.02 3.18 0.27 0.74 1.26 3,17 0.32 0.86 1.58 3.17 0.41 0.82 1.5 3.17 0.64 0.82 2.14 3.18 0.26 1.5 1.54 3.17 0.75 1.66 2.76 3.18 0.38 0.88 2.08 3.17 0.45 0.98 1.74 3.2 0.45 1.26 1.72 3.19 0.51 1.2 2.84 3.2 0.5 1.46 2.08 3.19 0.34 0.06 1.1 3.18 0.66 1.34 1.78 3.2 0.38 0.82 3.38 3.18 Category 9 1.82 1.86 1.31 1.57 1.18 1.66 1.22 1.54 1.41 2.24 1.66 1.66 1.38 2.3 2.02 Category 10 2.42 2.32 2.5 2.86 2.54 3.66 3.22 2.88 2.54 3.12 2.62 3.42 2.34 2.74 2.46 2.32 2.98 2.42 2.8 2.82 2.89 2.93 2.87 2.91 0.96 2.82 2.74 2.73 3.04 2.86 1.41 1.15 1.15 1.79