  Clustering large, mixed data is a central problem in data mining. Many
approaches adopt the idea of k-means, and hence are sensitive to
initialisation, detect only spherical clusters, and require a priori the
unknown number of clusters. We here develop a new clustering algorithm for
large data of mixed type, aiming at improving the applicability and efficiency
of the peak-finding technique. The improvements are threefold: (1) the new
algorithm is applicable to mixed data; (2) the algorithm is capable of
detecting outliers and clusters of relatively lower density values; (3) the
algorithm is competent at deciding the correct number of clusters. The
computational complexity of the algorithm is greatly reduced by applying a fast
k-nearest neighbors method and by scaling down to component sets. We present
experimental results to verify that our algorithm works well in practice.
Keywords: Clustering; Big Data; Mixed Attribute; Density Peaks;
Nearest-Neighbor Graph; Conductance.
