Several data mining algorithms have been tested, especially those that deal specifically with classification and outlier detection.
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.
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.
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.
Machine learning and data mining algorithms have been widely used in bioinformatics and computational biology [ 24– 28].
Cancer diagnosis has received considerable attention from researchers, and many classical data mining algorithms have been used in medical data analysis.
The data mining and clustering algorithm has been programmed using the Weka tool (University of Waikato, New Zealand), by implementing the Expectation Maximization (EM) clustering technique.
