Agglomerative clustering using improved rough sets and its applications in cooperative object localization

•A novel improved rough set agglomerative clustering.•Allowing the assignment of a class to the examples that fall in uncertain rules or do not fall into any rules.•Applicable for wireless multimedia sensor networks objects localization algorithm. Rough set has been applied to extract knowledge from various types of databases. Some limitations have been discovered in rough set, such as label inconsistency, the lack of flexibility and excessive dependency on discretization of the initial attributes. To overcome these limitations, a novel agglomerative clustering method using improved rough set is proposed. The idea of using equivalence class was also incorporated to merge and divide subclass. The experimental applications in data extraction and cooperative object localization showed the effectiveness of the presented improved rough set combined with agglomerative clustering.