Clustering Techniques for Categorical Data

Categorical data are generally thought to consist of contingency tables, which are data tables created whenever categorical data are cross-classified. Correspondence analysis is a statistical visualization method for picturing the associations between the levels of a two-way contingency table. In this work, the correspondence analysis for categorical data is analyzed using statistical and mathematical techniques when big data must be organized and analyzed due to the huge amount of data, and a MATLAB package (simple correspondence analysis) for this technique is presented.