Clustering customer dataset to find customer patterns
Suggestion: use an indicator variable for your "order"+"orderAmount" data. The table would look like this, which seems more suitable for distance metrics:

name      | tv | hifi | cd | timestamp
customer1 | 1  | 0    | 0  | 8:30
customer2 | 0  | 1    | 0  | 12:00
customer3 | 0  | 3    | 0  | 12:30
customer4 | 2  | 0    | 0  | 9:30
...


If you do so, then I'd also suggest to normalize those indicator variables (make sure they are in the interval [0, 1], otherwise you could have a order of 1000 "cd" completely dominating the maximum of 10 "tv" for example).

Another thing to watch out is the possibility of binning. If the time of the day doesn't matter much, you could group all the orders from the same day and customer in a single row.

Also, if you can manually label some examples, you could use a semi-supervised algorithm, and maybe it would have better performance than completely unsupervised clustering. Some possible algorithms are HMRF-KMeans, co-training variants, and Spy EM.