The remedian is an approximation of the median that can be computed using only $O(\log{n})$ storage. The algorithm was originally presented in The Remedian: A Robust Averaging Method for Large Data Sets by Rousseeuw and Bassett (1990). The core of it is on the first page:
Let us assume that $n = b^k$, where $b$ and $k$ are integers (the case where $n$ is not of this form will be treated in Sec. 7. The remedian with base $b$ proceeds by computing medians of groups of $b$ observations, yielding $b^{k1}$ estimates on which this procedure is iterated, and so on, until only a single estimate remains. When implemented properly, this method merely needs $k$ arrays of size $b$ that are continuously reused.
The implementation of this part in Clojure is so nice that I just had to share.
First, we need a vanilla implementation of the median function. We’re always going to be computing the median of sets of size $b$, where $b$ is relatively small, so there’s no need to get fancy with a linear time algorithm.
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Now we can implement the actual algorithm. We group, compute the median of each group, and recur, with the base case being when we’re left with a single element in the collection:
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Because partitionall
and map
both operate on and return lazy sequences, we maintain the property of only using $O(b \log_{b}{n})$ memory at any point in time.
While this implementation is simple and elegant, it only works if the size of the collection is a power of $b$. If we don’t have $n = b^k$ where $b$ and $k$ are integers, we’ll overweight the observations that get grouped into the last groups of size $< b$.
Section 7 of the original paper describes the weighting scheme you should use to compute the median if you’re left with incomplete groupings:
How should we proceed when the sample size $n$ is less than $b^k$? The remedian algorithm then ends up with $n_1$ numbers in the first array, $n_2$ numbers in the second array, and $n_k$ numbers in the last array, such that $n = n_1 + n_{2}b + … + n_k b^{k1}$. For our final estimate we then compute a weighted median in which the $n_1$, numbers in the first array have weight 1, the $n_2$ numbers in the second array have weight $b$, and the $n_k$ numbers in the last array have weight $b^{k1}$. This final computation does not need much storage because there are fewer than $bk$ numbers and they only have to be ranked in increasing order, after which their weights must be added until the sum is at least $n/2$.
It’s a bit difficult to directly translate this to the recursive solution I gave above because in the final step we’re going to do a computation on a mixture of values from the different recursive sequences. Let’s give it a shot.
We need some way of bubbling up the incomplete groups for the final weighted median computation. Instead of having each recursive sequence always compute the median of each group, we can add a check to see if the group is smaller than $b$ and, if so, just return the incomplete group:
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For example, if we were using the mutable array implementation proposed in the original paper to compute the remedian of (range 26)
with $b = 3$, the final state of the arrays would be:
Array  $i_0$  $i_1$  $i_2$ 

0  24  25  empty 
1  19  22  empty 
2  4  13  empty 
In our sequence based solution, the final sequence will be ((4 13 (19 22 (24 25))))
.
Now, we need to convert these nested sequences into [value weight]
pairs that could be fed into a weighted median function:
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Instead of weighting the values in array $j$ with weight $b^{j1}$, we’re weighting it at $\frac{b^{j1}}{b^{k}}$. Dividing all the weights by a constant will give us the same result and this is slightly easier to compute recursively, as we can just start at 1 and divide by $b$ as we descend into each nested sequence.
If we run this on the (range 26)
with $b = 3$, we get:
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Finally, we’re going to need a weighted median function. This operates on a collection of [value weight]
pairs:
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We can put it all together and redefine the remedian function to deal with the case where $n$ isn’t a power of $b$:
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The remedian is fun, but in practice I prefer to use the approximate quantile methods that were invented a few years later and presented in Approximate Medians and other Quantiles in One Pass and with Limited Memory by Manku, Rajagopalan, and Lindsay (1998). There’s a highquality implementation you can use in Clojure via Java interop in Parallel Colt’s DoubleQuantileFinderFactory.