These algorithms can be classified into three large categories namely frequent algorithms [23], maximum algorithms [3], and closed algorithms [24].
Also, we compare its parallel performances and accuracy against a parallel algorithm recently proposed for merging summaries derived by the Space Saving or Frequent algorithms.
As the goal of this work is to evaluate the quality of recommended routes by Web services and mining algorithms, the route recommendation algorithms (mining frequent path algorithms) used in this paper are reviewed first.
Since the earlier frequent pattern algorithm (e.g., apriori algorithm) needs to scan the whole dataset many times which is computationally very expensive.
Google is known in the search engine optimization community because of its frequent, manual algorithm updates that shake up rankings and frustrate webmasters.
Assuming that the current pattern is frequent, the algorithm extends it by appending one base at a time (Line 3), and constructs the corresponding projected database (Line 4).
Among the miscellaneous methods, the tree mining approach of [ 55] concentrates on the design of a general frequent tree algorithm and uses RNA based data as a minor application.
The support value of itemset (i 1, i 2 ) is defined as: Once the itemset is frequent, the algorithm will start to calculate the confidence value in order to decide whether it is a significant association rule R for a specific adherent group: { i 1, i 2 } → C 1 : The frequent itemsets were then filtered so that only maximum supersets remained.
If the extended pattern is frequent and closed (Algorithm 2), then the algorithm recursively calls itself with the extended pattern (Line 8).
In this paper we present and study a variety of distributed-memory parallel algorithms for a tree-projection-based frequent sequence discovery algorithm that are able to minimize the various overheads associated with load imbalance, database overlap, and interprocessor communication.
