Journal of Pattern Recognition Research

We present an online adaptive hierarchical clustering algorithm in a decision tree framework. Our model consists of an online adaptive binary tree and a code formation layer. The adaptive tree hierarchically partitions the data and the finest level clusters are represented by the leaf nodes. The code formation layer stores the representative codes of the clusters corresponding to the leaf nodes, and the tree adapts the separating hyperplanes between the clusters at every layer in an online adaptive mode. The membership of a sample in a cluster is decided by the tree and the tree parameters are guided by the stored codes. As opposed to the existing hierarchical clustering techniques where certain local objective function at every level is optimized, we adapt the tree in an online adaptive mode by minimizing a global objective functional. We use the same global objective functional as used in the fuzzy $c$-means algorithm (FCM), however, we observe that the effect of the control parameter is different from that in the FCM. In our model the control parameter regulates the size and the number of clusters whereas in the FCM the number of clusters is always constant ($c$). We never freeze the adaptation process. For every input sample, the tree allocates it to certain leaf node and at the same time adapts the tree parameters simultaneously with the adaptation of the stored codes. We validate the effectiveness of our model on certain real-life datasets and also show that the model is able to perform unsupervised classification on certain datasets.