A prediction system capable of predicting an unknown value of an attribute with high accuracy is provided. Based on first master data, second master data, and fact data indicating a relation between a first ID which is an ID of a record in the first master data and a second ID which is an ID of a record in the second master data, the co-clustering means 81 co-clusters the first IDs and the second IDs. The prediction model generation means 82 generates a prediction model for each cluster of the first ID output from the co-clustering means 81. When the first ID and the objective variable which is one of the attributes included in the first master data are specified, the prediction means 83 predicts the value of the objective variable corresponding to the first ID based on the prediction model and the belonging probability that the first ID belongs to each cluster.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A prediction system comprising: a co-clustering unit, implemented by a processor, that co-clusters first IDs and second IDs based on first master data, second master data, and fact data indicating a relation between the first ID which is an ID of a record in the first master data and the second ID which is an ID of a record in the second master data; a prediction model generation unit, implemented by the processor, that generates a prediction model for each cluster of the first ID output from the co-clustering unit; and a prediction unit, implemented by the processor, that, when the first ID and an objective variable which is one of attributes included in the first master data are designated, predicts a value of the objective variable corresponding to the first ID based on the prediction model and a belonging probability that the first ID belongs to each cluster. 2. The prediction system according to claim 1, wherein the prediction model generation unit generates, for each cluster of the first ID, a prediction model using an attribute in the first master data and a statistic of the value of the attribute in each record in the second master data determined to be related to the first ID by the fact data, as an explanatory variable. The prediction system according to claim 1, wherein the prediction unit specifies a cluster to which a designated first ID belongs, and predicts a value of an objective variable corresponding to the first ID. 4. The prediction system according to claim 1, wherein the prediction unit predicts a value of an objective variable corresponding to a designated first ID for each prediction model corresponding to each cluster of the first ID, and fixes a result of weighted addition of each predicted value with a belonging probability that the designated first ID belongs to each cluster as the value of the objective variable. 5. A prediction system comprising: a co-clustering unit, implemented by a processor, that co-clusters customers and products based on first master data including the customer and a customer attribute, second master data including the product and an attribute of the product, and fact data indicating a relation between the customer and the product; a prediction model generation unit, implemented by the processor, that generates a prediction model for each cluster of the customer output from the co-clustering unit; and a prediction unit, implemented by the processor, that, when the customer and an objective variable which is one of attributes of the customer are designated, predicts a value of an objective variable corresponding to the designated customer based on the prediction model and a belonging probability that the designated customer belongs to each cluster. 6. The prediction system according to claim 5, wherein the prediction model generation unit generates, for each cluster of the customer, a prediction model using an attribute of the customer and a statistic of the value of the attribute in each record in the second master data determined to be related to the customer by the fact data, as an explanatory variable. 7. A prediction method comprising: co-clustering first IDs and second IDs based on first master data, second master data, and fact data indicating a relation between the first ID which is an ID of a record in the first master data and the second ID which is an ID of a record in the second master data; generating a prediction model for each cluster of the first ID; and when the first ID and an objective variable which is one of attributes included in the first master data are designated, predicting a value of the objective variable corresponding to the first ID based on the prediction model and a belonging probability that the first ID belongs to each cluster. 8. The prediction method according to claim 7, wherein generating, for each cluster of the first ID, a prediction model using an attribute in the first master data and a statistic of the value of the attribute in each record in the second master data determined to be related to the first ID by the fact data, as an explanatory variable. 9. A prediction method comprising: co-clustering customers and products based on first master data including the customer and a customer attribute, second master data including the product and an attribute of the product, and fact data indicating a relation between the customer and the product; generating a prediction model for each cluster of the customer; and when the customer and an objective variable which is one of attributes of the customer are designated, predicting a value of the objective variable corresponding to the designated customer based on the prediction model and a belonging probability that the designated customer belongs to each cluster. 10. The prediction method according to claim 9, comprising generating, for each cluster of the customer, a prediction model using an attribute of the customer and a statistic of the value of the attribute in each record in the second master data determined to be related to the customer by the fact data, as an explanatory variable. 11. A non-transitory computer-readable recording medium in which a prediction program is recorded, the prediction program causing a computer to perform: co-clustering processing that co-clusters first IDs and second IDs based on first master data, second master data, and fact data indicating a relation between a first ID which is an ID of a record in the first master data and a second ID which is an ID of a record in the second master data; prediction model generation processing that generates a prediction model for each cluster of the first ID output from the co-clustering processing; and prediction processing that, when the first ID and an objective variable which is one of attributes included in the first master data are designated, predicts a value of an objective variable corresponding to the first ID based on the prediction model and a belonging probability that the first ID belongs to each cluster. 12. The non-transitory computer-readable recording medium according to claim 11, the prediction program causing a computer to generate, in the prediction model generation processing, for each cluster of the first ID, a prediction model using an attribute in the first master data and a statistic of the value of the attribute in each record in the second master data determined to be related to the first ID by the fact data, as an explanatory variable. 13. A non-transitory computer-readable recording medium in which a prediction program is recorded, the prediction program causing a computer to perform: co-clustering processing that co-clusters customers and products based on first master data including the customer and a customer attribute, second master data including the product and an attribute of the product, and fact data indicating a relation between the customer and the product; prediction model generation processing that generates a prediction model for each cluster of the customer output from the co-clustering processing; and prediction processing that, when the customer and an objective variable which is one of attributes of the customer are designated, predicts a value of an objective variable corresponding to the designated customer based on the prediction model and a belonging probability that the designated customer belongs to each cluster. 14. The non-transitory computer-readable recording medium according to claim 13, the prediction program causing a computer to generate, in the prediction model generation processing, for each cluster of the customer, a prediction model using an attribute of the customer and a statistic of the value of the attribute in each record in the second master data determined to be related to the customer by the fact data, as an explanatory variable.