Clusering based on categorical variables?
I assume your data set is sth like

A0 A1 A2 B0 B1 B2
1  0  0  1  1  0
0  1  0  0  0  1
0  1  0  0  1  1
      ...


where A0 refer to the answer of question A, choice 0 and question A is a single choice question,
B is a question allowing multiple answers.
Each row represents one record.

For this data format, each column can be considered as a dimension (if some columns can be quantified to a value, you may group them into one column, ex "Like = 1, Neutral = 0, Dislike = -1") Then applying clustering algorithms, such as K-mean to cluster it.

Please tell if your data set format is different from my assumption.

Updated on Jun 30:

The idea is to quantify the variables,
For column 2-5, the values are already quantified to [1, 5]
For column 5, it allows ("poor"/ "nice"). Although they are strings, they are representing the preference of different magnitude. They can also be converted to "poor" = 0, "nice" = 1.
For column 7-9, because it is a binary option, they can also be easily converted to ex. "true" = 0, "" = 1.

Then, you will have a matrix:

1    4    5    4    1    1    1    1
2    4    3    1    0    1    0    1
1    5    2    1    1    0    1    1
3    2    1    5    1    1    0    1


Remark1: For binary options, such as column 9, only allows "False" or "null", choosing "False" = 1 or 0 does not matter. In classification, only the distance matters.

Remark2: If you have a column allows different strings, such as "apple", "banana", "orange", you can convert it to an one hot vector.
Ex.

A
apple
orange
orange
banana


can be converted to

A0   A1   A2
1    0    0
0    0    1
0    0    1
0    1    0


where A0, A1, A2 represent "apple", "banana" and "orange".

Remark3: if there is a column allow storing multi answers, ex.

A
apple
apple orange
orange
orange banana


can be converted to
    A0   A1   A2
    1    0    0
    1    0    1
    0    0    1
    0    1    1

Remark4: before applying some classification algorithms such as K-mean, it is better to normalize the magnitude of each dimension. For example, value of column 9 is [0, 1] can be normalized by times 5 to [0, 5]. The factor/ the range of the dimension reflects the importance of that factor.

I hope my answer can help.