  This paper presents an effective method for fingerprint classification using
data mining approach. Initially, it generates a numeric code sequence for each
fingerprint image based on the ridge flow patterns. Then for each class, a seed
is selected by using a frequent itemsets generation technique. These seeds are
subsequently used for clustering the fingerprint images. The proposed method
was tested and evaluated in terms of several real-life datasets and a
significant improvement in reducing the misclassification errors has been
noticed in comparison to its other counterparts.
