Abstract: Aiming at the problem that the integration difficulty was relatively high caused by the low similarity and uncertainty of multi-source information data source domain, we proposed an integration method based on K-medoids clustering algorithm. First,  the clustering process of multi-source data was regarded as a transfer learning process, the weight value of the initial sample was determined, the learning characteristics of the weight and loss expectation value of the training sample in each iteration were recorded, and then the characteristic parameters were used to determine whether the data belongs to the source domain or the target domain. Then the clustering of the integration algorithm was transformed into a diversified domain value marking problem. After the data had the clustering characteristics, the weight factors between the data to be integrated in the source domain and the target domain were calculated respectively, the amount of interactive information between them was determined by using  the coverage characteristics of the weight factors, and the data with high amount of information was integrated to ensure the success rate of integration. The simulation experiment results show that the proposed algorithm  can achieve efficient integration, less secondary integration times and low overall consumption under stable and less datasets, or disordered and more and more complex datasets.

Key words: K-medoids clustering algorithm, multi\, source data, source domain, target domain, amount of interactive information