An efficient colossal closed itemset mining algorithm for a dataset with high dimensionality

Abstract

The greater interest of research in the field of bioinformatics and the ample amount of available data across the different domains paved the way for the generation of the dataset with high dimensionality. The number of features in the dataset with high dimensionality are very high and number of rows are less. The significance of the Frequent Colossal Closed Itemsets (FCCI) is high for diverse applications and also for the field of bioinformatics. FCCI are very prominent in the process of the decision making. Amount of information extraction from the dataset with high dimensionality is huge and this extraction is a non-trivial task. The pruning of all the inadmissible features and rows is not performed by the state-of-the-art algorithms. The proposed work articulates the pruning of all the inadmissible features and rows, an efficient pruning strategy to snip the row enumeration mining search space and closure method for checking the closeness of the rowset. An efficient row enumeration algorithm enclosing the rowset closure checking method and pruning strategy is designed to efficiently mine the complete set of FCCI. The experimental results demonstrate the effectiveness of pruning all the inadmissible features and rows.

Keywords

• Decision making
• Bioinformatics
• Rowset closeness checking
• Pruning strategy
• High dimensionality
• Mining search space