Local Algorithms For Interactive Clustering
The Journal of Machine Learning Research(2017)
摘要
We study the design of interactive clustering algorithms. The user supervision that we consider is in the form of cluster split/merge requests; such feedback is easy for users to provide because it only requires a high-level understanding of the clusters. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable properties in many applications. Local changes are desirable because in practice edits of other parts of the clustering are considered churn-changes that are perceived as quality-neutral or quality-negative. We show that in this framework we can still design provably correct algorithms given that our data satisfies natural separability properties. We also show that our framework works well in practice.
更多查看译文
关键词
interactive clustering,local algorithms
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要