  Clustering is an effective technique in data mining to generate groups that
are the matter of interest. Among various clustering approaches, the family of
k-means algorithms and min-cut algorithms gain most popularity due to their
simplicity and efficacy. The classical k-means algorithm partitions a number of
data points into several subsets by iteratively updating the clustering centers
and the associated data points. By contrast, a weighted undirected graph is
constructed in min-cut algorithms which partition the vertices of the graph
into two sets. However, existing clustering algorithms tend to cluster minority
of data points into a subset, which shall be avoided when the target dataset is
balanced. To achieve more accurate clustering for balanced dataset, we propose
to leverage exclusive lasso on k-means and min-cut to regulate the balance
degree of the clustering results. By optimizing our objective functions that
build atop the exclusive lasso, we can make the clustering result as much
balanced as possible. Extensive experiments on several large-scale datasets
validate the advantage of the proposed algorithms compared to the
state-of-the-art clustering algorithms.
