  In this paper, we introduce a novel Generic distributEd clustEring frameworK
(GEEK) beyond $k$-means clustering to process massive amounts of data. To deal
with different data types, GEEK first converts data in the original feature
space into a unified format of buckets; then, we design a new Seeding method
based on simILar bucKets (SILK) to determine initial seeds. Compared with
state-of-the-art seeding methods such as $k$-means++ and its variants, SILK can
automatically identify the number of initial seeds based on the closeness of
shared data objects in similar buckets instead of pre-specifying $k$. Thus, its
time complexity is independent of $k$. With these well-selected initial seeds,
GEEK only needs a one-pass data assignment to get the final clusters. We
implement GEEK on a distributed CPU-GPU platform for large-scale clustering. We
evaluate the performance of GEEK over five large-scale real-life datasets and
show that GEEK can deal with massive data of different types and is comparable
to (or even better than) many state-of-the-art customized GPU-based methods,
especially in large $k$ values.
