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Numerous variants of Self-Organizing Maps (SOMs) have been proposed in theliterature, including those which also possess an underlying structure, and insome cases, this structure itself can be defined by the user Although theconcepts of growing the SOM and updating it have been studied, the whole issueof using a self-organizing Adaptive Data Structure (ADS) to further enhance theproperties of the underlying SOM, has been unexplored. In an earlier work, weimpose an arbitrary, user-defined, tree-like topology onto the codebooks, whichconsequently enforced a neighborhood phenomenon and the so-called tree-basedBubble of Activity. In this paper, we consider how the underlying tree itselfcan be rendered dynamic and adaptively transformed. To do this, we presentmethods by which a SOM with an underlying Binary Search Tree (BST) structurecan be adaptively re-structured using Conditional Rotations (CONROT). Theserotations on the nodes of the tree are local, can be done in constant time, andperformed so as to decrease the Weighted Path Length (WPL) of the entire tree.In doing this, we introduce the pioneering concept referred to as NeuralPromotion, where neurons gain prominence in the Neural Network (NN) as theirsignificance increases. We are not aware of any research which deals with theissue of Neural Promotion. The advantages of such a scheme is that the userneed not be aware of any of the topological peculiarities of the stochasticdata distribution. Rather, the algorithm, referred to as the TTOSOM withConditional Rotations (TTOCONROT), converges in such a manner that the neuronsare ultimately placed in the input space so as to represent its stochasticdistribution, and additionally, the neighborhood properties of the neurons suitthe best BST that represents the data. These properties have been confirmed byour experimental results on a variety of data sets.

Self Organizing Maps Whose Topologies Can Be Learned With Adaptive Binary Search Trees Using Conditional Rotations