From Data to Information Granules: An Environment of Granular Computing

With enormous amounts of data come opportunities of building models of real-world systems that are instrumental in realizing a plethora of control, prediction, and classification tasks. The interpretability facet of ensuing models becomes highly relevant in light of designing autonomous systems and those constructs supporting human-centric decision-making environments. To transform data to tangible and actionable pieces of knowledge and formulate a problem at hand at a suitable level of abstraction, a convenient way to proceed is to position the problem in the environment of Granular Computing.We advocate that a systematic way of capturing knowledge residing within acquired data and encapsulating such knowledge in the form of interpretable models is supported by a suitable level of abstraction at which the data are to be represented. In turn, we show that an abstraction mechanism is conveniently realized in the form of information granules. Information granules and Granular Computing delivers an operational and flexible setting in which granular models are built and analyzed. A formal characterization of information granules is introduced where they are concisely described as triple (G, I, R) capturing their underlying geometry in the data space (G), information content (I), and representation capabilities of the underlying experimental evidence (R).A suite of design methods transforming data into information granules being articulated in various formal settings (e.g., intervals, fuzzy sets, rough sets) is analyzed and an array of generalizations is discussed (including collaborative ways of building granules in the presence of some auxiliary domain knowledge).In the sequel, it is shown how information granules regarded as functional modules are efficiently used in the construction of a vast array of interpretable models, especially rule-based architectures.