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.