Decision support in solving problems related to complex systems requires relevant computation models for the agents as well as methods for incorporating reasoning over computations performed by agents. Agents are performing computations on complex objects (e.g., (behavioral) patterns, classifiers, clusters, structural objects, sets of rules, aggregation operations, (approximate) reasoning schem...

A theory of granular computing is presented. Some classical example are examined to obtain the hint of new directions Obvious examples are dynamic programming, fuzzy numbers, infinitesimal number and access control model, (pre)topological spaces are examined; Localized multi-level granulation can be modeled by generalized topological spaces, called neighborhood systems. For most general granula...

This paper presents interpretations for association rules. It first introduces Pawlak’s method, and the corresponding algorithm of finding decision rules (a kind of association rules). It then uses extended random sets to present a new algorithm of finding interesting rules. It proves that the new algorithm is faster than Pawlak’s algorithm. The extended random sets are easily to include more t...

Granular computing (GrC) may be regarded as a label of theories, methodologies, techniques, and tools that make use of granules, i.e., groups, classes, or clusters of a universe, in the process of problem solving. The main objective of this paper is to discuss basic issues of GrC, with emphasis on the construction of granules and computation with granules. After a brief review of existing studi...

The theory of rough sets, proposed by Pawlak (1982, 1991, 1998; Pawlak and Skowron, 2007a, 2007b), offers systematic approaches for analyzing information tables in terms of indiscernibility, granulation and approximations. It has led to many useful and effective approaches for data analysis and machine learning. The notions of indiscernability and definability can be defined by a decision logic...

The main objective of this paper is to advocate for formal and mathematical modeling of data mining, which unfortunately has not received much attention. A framework is proposed for rule mining based on granular computing. It is developed in the Tarski’s style through the notions of a model and satisfiability. The model is a database consisting of a finite set of objects described by a finite s...

Uncertain knowledge management is a key issue in the expert system. By analysis of the reasons for knowledge’s uncertainty in complete universe and uncompleted universe, two methods based on set-theoretic granular computing are presented to deal with the uncertainty knowledge which is the rough observable method and rough inclusion method respectively. The rough set can be approximately express...

A granular computing model is used for learning classification rules by considering the two basic issues: concept formation and concept relationships identification. A classification rule induction method is proposed. Instead of focusing on the selection of a suitable partition, i.e., a family of granules defined by values of an attribute, in each step, we concentrate on the selection of a sing...