【摘要】 为了解决传统聚类方法处理高维稀疏数据对象时聚类结果不理想的问题,提出了SS/OSF聚类方法.该方法基于对象组相似度(SS)和对象组特征向量(OSF),并借助对象组特征向量的可加性实现.采用本方法得到高维稀疏数据对象的聚类结果后,可以根据聚类结果中各个对象集合的上确界和下确界为新对象进行对象组分类.实验表明,与传统K-means聚类方法相比,随着数据对象数目的增加,该方法无论是在运行时间上,还是在聚类结果的准确度方面都有明显的改进.更多还原

【Abstract】 Results of clustering are generally not ideal with traditional clustering method.Thus a SS/OSF clustering method is proposed for high-dimensional sparse data object based on set similarity(SS) and object set feature(OSF) with the addability of object set features.After the object clusters are gained by the SS/OSF clustering method,and according to the supremum and infimum of object clustering set,the new object can be distributed to all kinds of different clusters.Compared with the traditional K-means clustering method,the test results show that,as the number of object increases,the runtime and precision of results of the SS/OSF clustering method are seen to be clearly improved.更多还原