I have an interesting problem, and I think my Google is failing me since I can't find the same problem anywhere.

I have a set of individuals. I have 4 different datasets, with (some) to (all) of those individuals in any given set. So if I had 10 people, n=10, then maybe in DS1 I have all 10, DS2 has 7 of them, DS3 8, and DS4 5.

Each dataset DS# has it's own features. So maybe DS1 has 100 features, DS2 has 10, DS3 has 30, and DS4 also has 30.

I want to cluster the individuals who exist in any given dataset. So, if I did 3 clusters total, I'd have 3 clusters for DS1, 3 clusters for DS2, ... , 3 clusters for DS4.

I want a metric that captures how similar the results are. I'm doing this part for exploratory purposes. I'm also considering later incorporating this into a similarity metric for optimization purposes, but that's a further-out idea.

I however cannot find existing work that does something like this. Is there such work someone can point me to?

Much appreciated.


1 Answer 1



The closest line of work to your problem is Multi-View Clustering. Each data set DS is considered as a view, and views share a central entity (e.g. an individual).

2004 Multi-View Clustering is a survey on the topic. Also, here are two git projects from two papers (git 1, git 2) on the topic (they are implemented in Matlab and unfortunately are not production-ready).

A shortcut

Since most mainstream clustering methods (e.g. K-means, DBSCAN) accept a custom distance matrix $d(x, y)$ instead of explicit data vectors $x$ and $y$, partiality of features can be handled outside the method.

For example, suppose $x$ and $y$ are both present in views $1$ and $3$ but not view $2$. You may first build their vector by fusing their features from views $1$ and $3$, then computing their distance $d(x, y)$. Do the same for all pairs of individuals based on their common features, and finally feed the distance matrix $d(X, Y)$ to a clustering algorithm. This way, based on your domain knowledge, you can manually assign different weights to features of different views too. For example, you can define the distance as $$d(x, y) := \left \| x[f_1:f_3] - y[f_1:f_3] \right \| + 2\left \| x[f_4:f_5] - y[f_4:f_5] \right \|$$ which implies that the difference in view $3$ with features $f_4$ and $f_5$ is twice as important as the difference in view $1$ with features $f_1$ to $f_3$.

Comparing two partitions

If views are clustered separately, two partitions $p_1$ and $p_2$ respectively over views $1$ and $2$ can be compared using metric Normalized Variation of Information (NVI). If partitions are the same, it outputs $0$, and if they are statistically independent, it outputs $1$. A similar measure (that is not a metric) is Normalized Mutual Information (NMI).

If all individuals are not present in both partitions, either (1) remove non-shared individuals from both partitions, or (2) individuals in $p_1$ that are not present in $p_2$ can be assigned to an imaginary cluster $misc_2$ in $p_2$, the same for those in $p_2$ that are not in $p_1$ using $misc_1$ in $p_1$.


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