SS/OSF for High-Dimensional Sparse Data Object Clustering

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.