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The overall objective of this paper is to present a methodology for guiding adaptations of an RBF based relevance feedback network, embedded in automatic content-based image retrieval (CBIR) systems, through the principle of unsupervised hierarchical clustering. The self organizing tree map (SOTM) is essentially attractive for our approach since it not only extracts global intuition from an input pattern space but also injects some degree of localization into the discriminative process such that maximal discrimination becomes a priority at any given resolution. The main focus of this paper is two-fold: introducing a new member of SOTM family, the Directed SOTM (DSOTM) that not only provides a partial supervision on duster generation by forcing divisions away from the query class, but also presents a flexible verdict on resemblance of the input pattern as its tree structure grows; and modifying the current structure of the normalised graph cuts (Ncut) process by enabling the algorithm to determine appropriate number of clusters within an unknown dataset prior to its recursive clustering scheme through the principle of self-organizing normalized graph cuts (SONcut). Comprehensive comparisons with the Self-Organizing feature Map (SOFM), SOTM, and Ncut algorithms demonstrate feasibility of the proposed methods.