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The algorithm has been attracted a lot of attention in research communities, in which most of works try to improve clustering quality. Several thousands of papers have cited the DPC algorithm up to date. In this paper, we focus on the two following problems:
First problem, In real data set, the distribution of data may not follow some normal distributions. So it is not easy to match real cluster centers and peaks as DPC does. Second problem, In each local region of data set, it has several peaks so these peaks may belong to the same cluster, this problem can generate some mistakes in the choosing cluster center process. To tackle with these problems, in this paper we propose a new active density peak of clustering to improve clustering process by soliciting some labels from users to identify exactly the number of clusters for each data set. The experiments conducted on some UCI data sets show the effectiveness of our methods. |
IEEE/ICACT20220068 Slide.05
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