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Another simple method is to add each element of the list into a [[priority queue]] data structure, such as a [[heap (data structure)|heap]] or [[self-balancing binary search tree]], with at most ''k'' elements. Whenever the data structure has more than ''k'' elements, we remove the largest element, which can be done in O(log ''k'') time. Each insertion operation also takes O(log ''k'') time, resulting in O(''n'' log ''k'') time overall.
It is possible to transform the list into a [[binary heap]] in Θ(''n'') time, and then traverse the heap using a modified [[breadth-first search]] algorithm that places the elements in a [[priority queue]]{{clarify|reason=a heap is a priority queue!|date=January 2014}} (instead of the ordinary queue that is normally used in a BFS), and terminate the scan after traversing exactly ''k'' elements. As the queue size remains O(''k'') throughout the traversal, it would require O(''k'' log ''k'') time to complete, leading to a time bound of O(''n'' + ''k'' log ''k'') on this algorithm.{{citation needed|date=January 2014}}
== Optimised sorting algorithms ==
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