The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, common many applications. Mainstream approaches to missing reduce the problem formulation through either deletion or imputation these solutions may incur significant costs. Our k-POD method presents simple extension of for that works even when missingness mechanis...

Multiple kernel clustering aims to seek an appropriate combination of base kernels mine inherent non-linear information for optimal clustering. Late fusion algorithms generate partitions independently and integrate them in the following procedure, improving overall efficiency. However, separate partition generation leads inadequate negotiation with procedure a great loss beneficial correspondin...