I have a data set (>5000). each individual record of data is structured as a multilevel n-ary tree (>200 nodes). The tree node identifiers are unique within the tree. but the same identifiers are used to represent the same type of node across the data set. I would like to group the data set into multiple clusters based on the similarity between records. The records generally have a similar structure except some records have some branches pruned.
Here are some overly simplified examples.
basic type: A - B - C \- D - E - F \- G - H - I \- J sample 1: A \- D - E - F \- G - H - I \- J sample 2: A - B - C \- G - H - I \- J sample 3: A - B - C \- D - E - F \- G - H - I
I have no idea how many different types of tree structures are there in the dataset. I guess it's about 10-30. This is why I want to have a better understanding of the dataset by clustering the dataset. I want 'clustering' because I want to allow small variations in a cluster so that I could have a controllable number of clusters for analysis purposes.
any thought? thanks