Multiorder Neurons for Evolutionary Higher-Order Clustering and Growth

Kiruthika Ramanathan, Sheng-Uei Guan
2007 Neural Computation  
This article proposes to use Multi-Order Neurons for clustering irregularly shaped data arrangements. Multi-order neurons are an evolutionary extension of the use of higher order neurons in clustering. Higher order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher order tensors. The multi-order neuron goes one step further and eliminates two problems associated with higher order neurons. Firstly, it uses evolutionary algorithms to select the
more » ... hms to select the best neuron order for a given problem. Secondly, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically, we observed that, when the correlation of clusters-found with ground-truth-information is used in measuring clustering accuracy, the proposed evolutionary multi-order neurons method can be shown to outperform other related clustering methods. The simulation results from the IRIS, WINE and GLASS datasets show significant improvement when compared to the results obtained using SOMs and higher order neurons. The paper also proposes an intuitive model by which multi order neurons can be grown, thereby determining the number of clusters in a given data.
doi:10.1162/neco.2007.19.12.3369 pmid:17970658 fatcat:ixgqwhlfb5hsfk6vattnbt4x3e