I have a big set of subsets of 2 or more documents (maximum 10). The documents from each subset should describe the same product. One document contains pairs attribute - value:
<description>present box</description> <length>76 cm</length> <width>54 cm</width> <height>0.80 m</height> <weight>0.387 kg</weight>
For each subset I want to verify if the documents indeed describe the same product.
After extracting and converting quantitative values I ended up with a matrix like this:
length width height weight document1 76 54 80 387 document2 77 53 82 385 document3 85 65 87 411
Searching the web I found out that this could be a clustering problem but I couldn't figure out what clustering algorithms should I use. For k-means I have to provide the number of desired clusters and in this case I don't know it. For hierarchical clustering how should I interpret the dendrogram?
Because the question can be simplified to "There is one or more than one cluster?" where one cluster means the documents describe the same product and multiple clusters -> multiple products, which clustering algorithm should be applied for maximum accuracy? Or I am on the wrong direction from beginning and I have to differently address the problem?