Anzeige
Erschienen in:
2011 | OriginalPaper | Buchkapitel
A Mean Mutual Information Based Approach for Selecting Clustering Attribute
verfasst von : Hongwu Qin, Xiuqin Ma, Jasni Mohamad Zain, Norrozila Sulaiman, Tutut Herawan
Erschienen in: Software Engineering and Computer Systems
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Rough set theory based attribute selection clustering approaches for categorical data have attracted much attention in recent years. However, they have some limitations in the process of selecting clustering attribute. In this paper, we analyze the limitations of three rough set based approaches: total roughness (TR), min-min roughness (MMR) and maximum dependency attribute (MDA), and propose a mean mutual information (MMI) based approach for selecting clustering attribute. It is proved that the proposed approach is able to overcome the limitations of rough set based approaches. In addition, we define the concept of mean inter-class similarity to measure the accuracy of selecting clustering attribute. The experiment results show that the accuracy of selecting clustering attribute using our method is higher than that using TR, MMR and MDA methods.