  Distance-based clustering and classification are widely used in various
fields to group mixed numeric and categorical data. In many algorithms, a
predefined distance measurement is used to cluster data points based on their
dissimilarity. While there exist numerous distance-based measures for data with
pure numerical attributes and several ordered and unordered categorical
metrics, an efficient and accurate distance for mixed-type data that utilizes
the continuous and discrete properties simulatenously is an open problem. Many
metrics convert numerical attributes to categorical ones or vice versa. They
handle the data points as a single attribute type or calculate a distance
between each attribute separately and add them up. We propose a metric called
KDSUM that uses mixed kernels to measure dissimilarity, with cross-validated
optimal bandwidth selection. We demonstrate that KDSUM is a shrinkage method
from existing mixed-type metrics to a uniform dissimilarity metric, and
improves clustering accuracy when utilized in existing distance-based
clustering algorithms on simulated and real-world datasets containing
continuous-only, categorical-only, and mixed-type data.
