The dendritic cells algorithm (DCA) is an algorithm which simulates antigen presentation of dendritic cells in biological sciences. As a binary classifier, the DCA can classify input data items as normal and abnormal one quickly and efficiently. DCA uses the Principal Component Analysis (PCA) to do feature extraction and signal categorization. However, using PCA presents a limitation as the signals extraction are depend on artificial determination. To overcome this limitation, this study proposes a numerical differentiation based dendritic cell model. The proposed model introduces numerical differentiation theory which can describe that data changes will lead to danger, and extracts signals adaptively, through deep data analysis with respect to change and adaptive. Indeed, DCA is sensitive to the input data sequence for each DC gathers multiple antigens over a period of time. Therefore, the study proposes a numerical differentiation based DCA (NDDCA). DC samples antigen randomly and dynamically to overcome the sensitivity to the order of input data, which makes the classification of NDDCA clearer and only focus on the classification data source. Experiments on real data sets show that our new approach which focuses on unordered data binary classification problems delivers more accurate results.