Fast answear Mean Shift LSH which is an upgrade in $O(n)$ of the famous Mean Shift algorithm in $O(n^2)$ well know for its image segmentation ability Some explanations If you desire a true ...

I think what the author is speaking about is the time/memory complexity of algorithms that statisticians may don't care about. Make a model which is mathematically well proven may be more important to ...

For 2 tables of size $n$ and $m$, join operation complexity is in $O(n.m)$ in worst case. If $n=m$ we are facing a quadratic operation which is why you observe slowness with the increase of tables ...

As @Anony-Mousse pointed it, on DBSCAN index structures are often used in order to decrease execution times. K-d-trees are one example but this one works well just in small dimensions. You had right ...

Broadcasting your data and learn on it with different learning parameters per spark partition is a solution only if your data isn t so big it can fit in each machine memory. If you desire apply ML ...

If you don t know the number of clusters, i encourage you to look at those density based algorithm : Mean Shift, DBSCAN, OPTICS. They don t presume of the cluster number and are able to find random ...

For the first you can do as follow : val discount = salesdata.map( str => str.split(",")) .map( array => (array(0), array(1), array(2), array(3).toDouble) ) ...

If you have exclusively binary variable you can use KModes, if you have both real and binary variables I would consider the KPrototypes algorithm. KModes use by default the hamming distance and ...

KNN algorithm is a solution but it doesn't scale well. Cost of KNN is $O(n.log(n))$ per request if you made $n$ ones it become $O(n^2.log(n))$ and don't scale at all. It is applicable if your cluster ...

As @Anony-Mousse remind it, compute a similarity matrix is note applicable to large dataset. Memory complexity is linked to time complexity, if you want to read or writte $n$ value you will require at ...

As @Anony-Mousse specify it you need a distance function fitting with your data nature. You have multiple possibilities, convert all your data as categorical, more specifically as binary data ...

If you know the ground truth of data, the ethnic here. You can visualize your binary cluster as follow. Compute prototypes of each cluster using majority vote per feature which has a linear complexity ...

I fall on same issue with RMSE which by the way may be a good complementary choice of MAE. Thus in order to measure error prediction on multidimentional output the way i implemented was as follow. ...