TFSDG 2013 - Special Session on Type-2 Fuzzy Sets in Data Granulation

Granular computing as a general theory of computation is about representing information in terms of some aggregates and their processing. The predominant technique for information granulation is through using clustering-based algorithms. However, since the main aim of the information granulation is the aggregation of low level entities by means of some similarity, proximity etc. criteria, the granulation technique, for instance, can be generalized through compression or (fuzzy) relation based techniques. Moreover, other novel methodologies are expected to be proposed to put different formal frameworks of granular computing into practice of information granulation: in particular, type-2 fuzzy sets.
The objective of this special session is to embrace novel methodologies on granulating data into general and interval type-2 fuzzy sets and their operations. There is also an elaboration on the design of granular computing methods for analyzing type-2 fuzzy sets conceived as patterns of datasets. Efficient procedures for dealing with information granules modeled with general and interval type-2 fuzzy sets are expected. Such algorithms enable new possibilities for application of type-2 fuzzy sets in the context of pattern analysis and machine intelligence. Both theoretical and experimental works are emphasized; moreover, researches that connect experiments to the theory are particularly welcome.