Audio signals are a type of high-dimensional data, and their clustering is critical. However, distance calculation\nfailures, inefficient index trees, and cluster overlaps, derived from the equidistance, redundant attribute, and sparsity,\nrespectively, seriously affect the clustering performance. To solve these problems, an audio-signal clustering\nalgorithm based on the sequential Psim matrix and Tabu Search is proposed. First, the audio signal similarity is\ncalculated with the Psim function, which avoids the equidistance. The data is then organized using a sequential\nPsim matrix, which improves the indexing performance. The initial clusters are then generated with differential\ntruncation and refined using the Tabu Search, which eliminates cluster overlap. Finally, the K-Medoids algorithm is\nused to refine the cluster. This algorithm is compared to the K-Medoids and spectral clustering algorithms using\nUCI waveform datasets. The experimental results indicate that the proposed algorithm can obtain better Macro-F1\nand Micro-F1 values with fewer iterations.
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