# string clustering: similarity criterion

I have a set of strings of dimension $10,000$. I want to group similar strings together in one group, perform clustering. As string metric, I am using the Levenshtein distance.

Simply, with the Levenshtein distance I'll just compute distance between $2$ strings and then by using a threshold the clustering algorithm will make the decision if they can be grouped or not. This is not enough. I am looking for a special measure to study the relation between the strings.

For example: door and entrance wont be grouped together if I just compute the Levenshtein distance, in fact there is nothing in common between these $2$ words. But logically they are connected and can be grouped together, since door and entrance are basically the same.

• Have you ever come across such a problem?
• Is it similar to the Semantic similarity measure?