Abstract
When using topographic maps for clustering purposes, which is now being considered in the data mining community, it is crucial that the maps are free of topological defects. Otherwise, a contiguous cluster could become split into separate clusters. We introduce a new algorithm for monitoring the degree of topology preservation of kernel-based maps during learning. The algorithm is applied to a real-world example concerned with the identification of 3 musical instruments and the notes played by them, in an unsupervised manner, by means of a hierarchical clustering analysis, starting from the music signal's spectrogram.
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Van Hulle, M.M., Gautama, T. Monitoring the Formation of Kernel-Based Topographic Maps with Application to Hierarchical Clustering of Music Signals. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 32, 119–134 (2002). https://doi.org/10.1023/A:1016323603757
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DOI: https://doi.org/10.1023/A:1016323603757