Most of the previous works that disambiguate personal names in Web search results often employ agglomerative clustering approaches.
In contrast, we have adopted a semi-supervised clustering approach in order to guide the clustering more appropriately. Our
proposed semi-supervised clustering approach is novel in that it controls the fluctuation of the centroid of a cluster, and
achieved a purity of 0.72 and inverse purity of 0.81, and their harmonic mean F was 0.76.
Keywords Information retrieval - Semi-supervised clustering - Personal name disambiguation