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Based on KEP-Index, the time complexity for finding the top-r weighted k-truss communities is linear to the size of these communities, thus it is optimal.
To reduce the computational burden, a heuristic algorithm with polynomial time complexity for finding a unique (relative) reduct is designed by using the inner and outer significance measures of each criterion candidate.
Hence, the time complexity for finding n occurrences of S is O(| S|+ n).
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To cope with high time complexity of finding an optimal decomposition, we propose a suboptimal heuristic algorithm.
Time complexity for the mentioned method is (N^3).
Time Complexity The training time complexity for SVM is dominated by the time for solving the underlying quadratic program.
Time complexity for pattern search is reduced using beam pruning techniques.
We will now discuss the time complexity for pattern search (Algorithm B).
Thus, the total time complexity for the seeds extraction step is O(n).
In the second step, the time complexity for agglomerative hierarchical algorithm is O (m 3 ).
In this case, the time complexity for least squares fitting can be estimated as O(m).
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