Growing neural gas for temporal clustering

Conventional clustering techniques provide a static snapshot of each vector's commitment to every group. With additive datasets, however, existing methods may not be sufficient for adapting to the presence of new clusters or even the merging of existing data-dense regions. To overcome this deficit, we explore the use of growing neural gas for temporal clustering and provide evidence that this new algorithm is capable of detecting cluster structures that incrementally emerge.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Isaac J. Sledge, James M. Keller
• Keywords: Additive Datasets, Computer Vision, Conventional Clustering Techniques, ICPR 2008, Temporal Clustering