An analysis and literature review of algorithms for frequent itemset mining

The data mining process should be led by domain knowledge. It includes different aspects including the selection of the data, interpretation, extraction, and transformation. In this paper different domains have been covered for the analysis of various data mining algorithms. The main emphasis on the algorithms which are mainly used for the extraction and discovering of interesting patterns and relationships. Various data mining algorithms, such as sequential pattern discovery using equivalence classes (SPADE), k-means, Apriori algorithm, FP-Growth and others, were discussed in this paper. The reviews and analysis of the advantages and disadvantages of various data mining approaches have been explored with advantages and limitations. In summary, this paper provides a comprehensive understanding of data mining approaches and their potential applications in various fields.