Name
Abstract | ||
|---|---|---|
We present an extension of the DP-means algorithm, a hard-clustering approximation of nonparametric Bayesian models. Although a recent work [6] reports that the DP-means can converge to a local minimum, the condition for the DP-means to converge to a local minimum is still unknown. This paper demonstrates one reason the DP-means converges to a local minimum: the DP-means cannot assign the optimal number of clusters when many data points exist within small distances. As a first attempt to avoid the local minimum, we propose an extension of the DP-means by the split-merge technique. The proposed algorithm splits clusters when a cluster has many data points to assign the number of clusters near to optimal. The experimental results with multiple datasets show the robustness of the proposed algorithm. |
Year | Venue | Field |
|---|---|---|
2016 | ECML/PKDD | Data point,Cluster (physics),Computer science,Nonparametric bayesian,Algorithm,Robustness (computer science),Maxima and minima,Cluster analysis,Merge (version control) |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
21 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shigeyuki Odashima | 1 | 21 | 2.72 |
| Miwa Ueki | 2 | 4 | 1.45 |
| Naoyuki Sawasaki | 3 | 17 | 4.35 |