Clustering by Constructing Hyper-Planes
This is an incremental improvement in clustering algorithms for data analysis.
The authors tackled the problem of clustering by developing an algorithm that uses hyper-planes to distinguish data points based on marginal spaces, and it outperformed other methods on benchmark datasets.
As a kind of basic machine learning method, clustering algorithms group data points into different categories based on their similarity or distribution. We present a clustering algorithm by finding hyper-planes to distinguish the data points. It relies on the marginal space between the points. Then we combine these hyper-planes to determine centers and numbers of clusters. Because the algorithm is based on linear structures, it can approximate the distribution of datasets accurately and flexibly. To evaluate its performance, we compared it with some famous clustering algorithms by carrying experiments on different kinds of benchmark datasets. It outperforms other methods clearly.