Modeling the binding of transcription factors helps to decipher the control logic behind transcriptional regulatory networks. Position weight matrix is commonly used to describe a binding motif but assumes statistical independence between positions. Although current approaches take within-motif dependence into account for better predictive performance, these models usually rely on prior knowledge and incorporate simple positional dependence to describe binding motifs. The inability to take complex within-motif dependence into account may result in an incomplete representation of binding motifs. In this work, we applied association rule mining techniques and constructed models to explore within-motif dependence for transcription factors in Escherichia coli. Our models can reflect transcription factor-DNA recognition where the explored dependence correlates with the binding specificity. We also propose a graphical representation of the explored within-motif dependence to illustrate the final binding configurations. Understanding the binding configurations also enables us to fine-tune or design transcription factor binding sites, and we attempt to present the configurations through exploring within-motif dependence.