CAJ | 학술논문

提出基于聚类及链接分析的挖掘模型LinkNetClus，该模型将对象类型分为目标类型及属性类型，并假设目标对象属于每个簇的概率依赖于与之相关的其他对象，在目标对象上进行迭代的聚类操作，最终得到具有多样性的聚类结果。该模型充分利用了异质信息网络中的关联关系，得到多维的挖掘结果来解决数据冗余的问题，结果的可解释性也优于排序序列。通过实验证明了使用LinkNetClus得到的聚类结果比已有的基准方法提高大概30％～50％左右。
제출기우취류급련접분석적알굴모형LinkNetClus，해모형장대상류형분위목표류형급속성류형，병가설목표대상속우매개족적개솔의뢰우여지상관적기타대상，재목표대상상진행질대적취류조작，최종득도구유다양성적취류결과。해모형충분이용료이질신식망락중적관련관계，득도다유적알굴결과래해결수거용여적문제，결과적가해석성야우우배서서렬。통과실험증명료사용LinkNetClus득도적취류결과비이유적기준방법제고대개30％～50％좌우。
In this paper, the author proposes a mining model named LinkNetClus based on clustering and link analysis.Firstly, a star network is constructed, which has attribute objects and target objects. Assume that the generation probability of a target object is based on these associated attribute objects. Then these target objects will be clustered and get diversity results.The LinkNetClus model also uses the homogeneous and heterogeneous relationship between objects, and gets multidimensional ranking result to eliminate redundant information.Compared with the two -phrase ranking result, the result made by LinkNetClus is more understandable.Compared with the existing base method, experiments show that the mining results based on LinkNetClus model is enhanced as many as 30%~50%.