Best vector distance measure for contrasting clustering?

Let say i have V-vectors, everyone with size N, which measure of distance should I use if i want to cluster them by the following criteria : the more elements vector has in common with other vectors of the cluster and the fewer elements in common with members of the contrasting clusters.

Ideally the number of clusters are dynamic and the vectors are added one-by one. But I don't think this have impact on the distance measure.

I found Tversky contrast distance ! It seems to be a Set distance metric, whatever that means. On the other hand the formula looks nice.

d(x,y) = a * f(x intersect y) - b * f(x-y) - c * f(y-x)

in binary :

d(x,y) = a * count(x&y) - b * count(x&~y) - c * count(~x&y)

It's not clear if your "vector" is an unordered set of $N$ discrete objects, but that's what it seems like. You can simply define a distance metric e.g. like: $$d(x,y) = \left[\gamma+ \sum_i \sum_j L[x_i,y_j] \right]^{-2}$$ where $\gamma\in\mathbb{R}^+$ and $$L[x_i,y_j] = \begin{cases} 1 &\text{if} \;\;x_i = y_j\\ 0 & \text{otherwise} \end{cases}$$ Or if you view each $x$ as a set, you can use something like: $$\tilde{d}(x,y) = \left( \gamma + \frac{1}{N}\sum_k {1}_{x_k\in y} \right)^{-1}$$

Then run pretty much any clustering algorithm using this metric.

As for the clustering being dynamic, you basically have to use a clustering algorithm that does not take the number of clusters as inputs. Algorithms like mean shift, affinity propagation, dbscan, or a Bayesian Gaussian mixture with a Dirichlet prior. You can get coordinates, if desired, by using the distance matrix $D$, where $D_{ij} = d(x_i,x_j)$, with an embedding algorithm like multidimensional scaling.

These two questions ( and ) are related. Some nice papers I saw that can do dynamic estimation of the number of clusters with online updates are:

• Sequential clustering with particle filtering - Estimating the number of clusters from data by Schubert & Sidenbladh

• Revisiting k-means: New Algorithms via Bayesian Nonparametrics by Kulis & Jordan

• Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture by Campbell et al

• Streaming Clustering with Bayesian Nonparametric Models by Huynh & Phung

• thanks, can you elaborate more on dynamic clustering ? – sten Jun 26 '18 at 16:14
• @user1019129 for instance, look at affinity propagation, you just need to create a similarity matrix, e.g. $S_{ij} = D_{ij}^{-1}$, and pass it into the algorithm. The Tversky distance looks nice. – user3658307 Jun 26 '18 at 16:22
• by dynamic clustering I meant when the data comes one-by-one i.e. the number of clusters and which vector is in which cluster is dynamic. Is this what you meant too ? – sten Jun 26 '18 at 16:26
• @user1019129 No, I was referring to your phrase "the number of clusters is dynamic". When the vectors come as a stream, the phrase most common in the literature is online clustering or sequential clustering. Googling it will show you some example algorithms. They tend to be more complicated than standard clustering, especially if you also want the number of clusters to be non-stationary ("dynamic"). I'll add a couple references. – user3658307 Jun 26 '18 at 17:18