The Rough Topology for Numerical Data

Abstract

In this paper, we generalize the rough topology and the core to numerical data by classifying objects in terms of the attribute values. A new approach to finding the core for numerical data is discussed. Then a measurement to find whether an attribute is in the core or not is given. This new method for finding the core is used for attribute reduction. It is tested and compared by using eight different machine-learning algorithms. Also, it is discussed how this material is used to rank the importance of attributes in data classification. Finally, the algorithms and codes to convert data to pertinent data and to find the core is also provided.