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Moreover, when it comes to temporal series traditional data mining algorithms have showed limitations.
Other association rule mining algorithms have been widely used in the literature to extract frequent itemsets and build decision rules.
Several data mining algorithms have been tested, especially those that deal specifically with classification and outlier detection.
With the aim of satisfying the needs of users and improving the efficiency and effectiveness of mining task, many various constraints and mining algorithms have been proposed.
Moreover, some data mining algorithms have been applied to reduce the huge amount of raw data, to recognize patterns for analysis, and to learn the given parameters.
The time complexities for both sequential and parallel genetic-fuzzy mining algorithms have also been analyzed, with results showing the good effect of the proposed one.
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This kind of mining algorithm has been largely applied in e-commerce applications, presenting good results.
A fuzzy mining algorithm has then been proposed, which is based on the AprioriAll algorithm, but different from it in several ways.
While biclustering is not exclusively a frequent itemset mining problem, frequent itemset mining-based algorithms have been shown to perform equally or superior to various other methods [ 29].
Recent years have observed increasing efforts on graph mining and many algorithms have been developed for this purpose.
> Data mining algorithms (DMAs) have been developed for screening spontaneous report databases (SRDs).
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