Adaptive resonance theory (ART) networks, including developmental resonance network (DRN), basically use a vigilance parameter as a hyperparameter to determine whether a current input can belong to any existing categories or not. The problem here is that the clustering quality of those networks is sensitive to the vigilance parameter so that the users are required to fine-tune the parameter delicately beforehand. Another problem is that those networks only deal with a hyperrectangular decision boundary, which means they cannot learn categories of arbitrary shape. In addition, the order of data processing is a critical factor to categorize clusters correctly because each category can expand its boundary into the areas of other categories erroneously. To deal with these problems, we propose an advanced version of DRN, Adaptive DRN (A-DRN), which learns the vigilance parameters assigned for individual category nodes as well as category weights. The proposed A-DRN combines close categories to construct a cluster that contains the categories identifying a cluster boundary of arbitrary shape. Our A-DRN also employs a sliding window. The sliding window buffers sequential data points to presume the data distribution roughly, which helps our network to have a robust and consistent performance to a random order of input data. Through the experiments, we empirically demonstrate the effectiveness of A-DRN in both synthetic and real-world benchmark data sets.