关键词: 实体解析, 无监督聚类, 无监督增量聚类

Abstract: The aim is to analyze the mechanism of Entity Resolution（ER） from unsupervised clustering. This paper firstly elaborates the unsupervised clustering ideas from specific types, classical algorithms; then, it studies the unsupervised incremental clustering method from the classical algorithm improvements and evolution analyses. Finally, the problems to be solved in unsupervised clustering are prospected. Unsupervised clustering technology not only can solve the clustering efficiency and quality problems of traditional entity resolution, but also can use existing clustering results to implement incremental entity resolution for rapidly evolving data, to further meet the needs for incremental incremental entity resolution under the big data environment. There is no in-depth analysis of the evaluation index of unsupervised clustering algorithm. Although the unsupervised clustering method for entity analysis has many advantages, it still faces the challenges of accuracy and scalability.

Key words: Entity Resolution（ER）, unsupervised clustering, unsupervised incremental clustering