In many applications, classification labels may not be associated with a single instance of records, but may be associated with a data set of records. The class behavior may not be possible to infer effectively from a single record, but may be only be inferred by an aggregate set of records. Therefore, in this problem, the class label is associated with a set of instances both in the training and test data. Therefore, the problem may be understood to be that of classifying a set of data sets. Typically, the classification behavior may only be inferred from the overall patterns of data distribution, and very little information is embedded in any given record for classification purposes. We refer to this problem as the setwise classification problem.

The problem can be extremely challenging in scenarios where the data is received in the form of a stream, and the records within any particular data set may not necessarily be received contiguously. In this paper, we present a first approach for real time and streaming classification of such data. We present experimental results illustrating the effectiveness of the approach.