Using Context Transformations as a Pre-Processing Step in Mining Large Datasets

Data mining is being applied in several diverse areas such as market basket analysis, analysis of dependencies in biological sequences, search and extraction of information in the web, predicting trends in stock market and many others. In such applications, one of the methods of data analysis, that is gaining recognition is Formal Concept Analysis (FCA). The use of FCA in representing large datasets is particularly promising in reducing the time and storage representation. The characteristic that distinguishes FCA from other analysis methods is the absence of loss of information during the analysis of data. Discusses two context transformations that do not change the structure of the concept lattice, namely context clarification and reduction. The objective is to explore the possibility of using such transformations as a preprocessing step so that, the dataset can be represented as a reduced context.