Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Formal concept analysis in information science
Annual Review of Information Science and Technology
The rough set exploration system
Transactions on Rough Sets III
| Hi-index | 0.00 |
Data in an information system are usually represented and stored in a flat and unconnected structure as in a table. Underlying the data structure, there is a domain concept that is an understandable description for humans and supports other machine learning techniques. In this work, Map Granule (MG) construction is introduced. A MG comprises of multilevel granules with their hierarchy relations. We propose a rough set based granular computing to induce approximation of a domain concept hierarchy of an information system. An algorithm is proposed to select a sequence of attribute subsets which is necessary to partition a granularity hierarchically. In each level of granulation, reducts and core are applied to retain the specific concepts of a granule whereas common attributes are applied to exclude the common knowledge and generate a more general concept. The information granule relations are represented by a tree structure in which the relation strengths are defined by a rough ratio of specificness/coarseness.