Selected insights into building data associations and their granular augmentations

Data are the blood life of today's society. Revealing interpretable and conceptually stable associations (relationships) within data forms one of the central items on the agenda of data analytics. With this regard, associative memories capturing linkages between data positioned in two (or more) data spaces are examples of commonly considered architectures. In contrast to the existing constructs of memories, we propose a new category of architectures that exhibits several essential features: (i) associative mapping is spanned over a collection of prototypes (viz. representatives) of data and in this way becomes focused on their structural essentials, (ii) prototypes are built through a process of collaborative clustering, (iii) the design process retains privacy aspects by not disclosing locally available data, (iv) optimization of bidirectional and multidirectional recall is presented. In the sequel, we demonstrate how granular mappings engaging granular parameter spaces are developed and assessed. Associative relationships constructed in terms of granular bidirectional and multidirectional associative memories are investigated. We also develop granular autoencoders and stacked granular auto encoders offering further support of the development process.