Understanding of interactions is the critical issue of complex systems. Interactions in physical world are represented by information granules. We propose to model complex systems by interactive intelligent systems (IIS) created by societies of agents. Computations in IIS are based on complex granules (c-granules, for short). Adaptive judgement allows us to reason about c-granules and interacti...

This chapter gives an overview and refinement of recent works on binary granular computing. For comparison and contrasting, granulation and partition are examined in parallel from the prospect of rough Set theory (RST).The key strength of RST is its capability in representing and processing knowledge in table formats. Even though such capabilities, for general granulation, are not available, th...

A bottle up hyperbox granular computing (HBGrC) is developed based on distance measure. Firstly, hyperbox granule is represented by the beginning point and the end point. Secondly, the distance measure between two hyperbox granules is defined by the beginning points and the end points. Thirdly, operations between two hyperbox granules are designed to the transformation between two hyperbox gran...

A real world fuzzy set should be able to tolerate ”small amounts” of perturbations. In type II fuzzy set represents each grade is represented by a fuzzy number. In other words, each grade may be perturbed independently. Such an ”independent perturbation” is inadequate, when a membership function is required to be continuous, differentiable, and etc. Pointwise perturbation cannot guarantee the c...

This paper examines granulation structures for stratified rough set approximations. With respect to different level of granulations, various approximations are obtained. Two special types of granulation structures are investigated. A nested sequence of granulations by equivalence relations leads to a nested sequence of rough set approximations. A hierarchical granulation, characterized by a spe...

The problem of image object extraction in the framework of rough sets and granular computing is addressed. A measure called ‘‘rough entropy of image’’ is defined based on the concept of image granules. Its maximization results in minimization of roughness in both object and background regions; thereby determining the threshold of partitioning. Methods of selecting the appropriate granule size a...

What is Deductive Data Mining (DDM)? It is a methodology that derives patterns from the mathematical structure of the stored data. Among three core techniques of data mining [1], classifications and association rule mining are deductive data mining, while clustering is not, because its algorithms often use some properties of the ambient space. What is Granular Computing (GrC)? In general, it is...

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular ...

Extended Abstract. We discuss an approach to modeling of computations performed by Complex Adaptive Systems (CAS) based on Interactive Granular Computing (IGrC). Complex adaptive systems (CAS) are made up of multiple interacting elements and have the capacity to change and learn from experience. The key problems of complex systems are difficulties with their formal modeling and simulation. Some...

In opinion analysis, opinion target extraction is the basic component. Dealing with the opinion target extraction on short comments, we adopt two-dimensional vectors to represent words, which are composed of the frequency of the word and the proportion the word companying with subjective words respectively. Neural network is adopted in the opinion target extraction process, and the method is te...