Because of the patient’s inconsistent data, uncertain Thyroid Disease dataset is appeared in the learning process: irrelevant, redundant, missing, and huge features. In this paper, Rough sets theory is used in data discretization for continuous attribute values, data reduction and rule induction. Also, Rough sets try to cluster the Thyroid relation attributes in the presence of missing attribut...

This paper concerns the relationship between rough sets and flow graphs. It is shown that flow graph can be used both as formal language for computing approximations of sets in the sense of rough set theory, and as description tool for data structure. This description is employed next for finding patterns in data. To this end decision algorithm induced by the flow graph is defined and studied.

Reuse of graywater is often suggested to be amogts the most effective to some of the current ecological problems. Application of rough sets theory for discovering dependances between various types of graywater is in the main focus of this work. This can facilitate processes aiming at less energy and chemical use and lower fresh water use. Key–Words: Rough sets, order ideals, quasi-ordered set

Granularity of knowledge attracted attention of many researchers recently. This paper concerns this issue from the rough set perspective. Granularity is inherently connected with foundation of rough set theory. The concept of the rough set hinges on classification of objects of interest into similarity classes, which form elementary building blocks (atoms, granules) of knowledge. These granules...

Clustering, rough sets and decision tree theory were applied to the evaluation of soil fertility levels ,and provided new ideas and methods among the spatial data mining and knowledge discovery. In the experiment, the rough sets decision tree evaluation model establish by 1400 study samples, the accuracy rate is 92% of the test. The results show :model has good generalization ability; using the...

Rough set theory is a powerful tool for dealing with uncertainty, granularity, and incompleteness of knowledge in information systems. In addition, digital topology deals with properties and features of two-dimensional or three-dimensional digital images that correspond to topological properties of objects. So, we try to describe the relationship between rough sets and digital topology. Firstly...

The whole mathematical scenario has changed with the advent of the Rough Set Theory, a powerful tool to deal with uncertainty and incompleteness of knowledge in information system. With the advancement of research, the Soft Set Theory has emerged as an advanced mathematical tool to deal with data associated with uncertainty. The present chapter endeavors to forge a connection between soft set a...

One of the main obstacles facing current machine learning techniques is that of dataset dimensionality. Usually, a redundancy-removing step is carried out beforehand to enable these techniques to be effective. Rough Set Theory (RST) has been used as such a dataset pre-processor with much success, however it is reliant upon a discretized dataset; important information may be lost as a result of ...

One of the main obstacles facing current fuzzy modelling techniques is that of dataset dimensionality. To enable these techniques to be effective, a redundancy-removing step is usually carried out beforehand. Rough Set Theory (RST) has been used as such a dataset pre-processor with much success, however it is reliant upon a crisp dataset; important information may be lost as a result of quantiz...

`Feature selection aims to remove features unnecessary to the target concept. Rough-set theory (RST) eliminates unimportant or irrelevant features, thus generating a smaller (than the original) set of attributes with the same, or close to, classificatory power. Clustering, also a form of data grouping, groups a set of data such that intra-cluster similarity is maximized and inter-cluster simila...