1. A horizontal anomaly detection method comprising: receiving a plurality of descriptions describing a plurality of objects, each object of the plurality of objects being described by a plurality of different information sources, wherein each individual information source of the plurality of information sources captures a plurality of similarity relationships between the plurality of objects; generating a similarity matrix from the plurality of different information sources, wherein entries of the similarity matrix represent quantitative scores of similarity between pairs of the plurality of objects; and identifying at least one horizontal anomaly within the plurality of objects from the similarity matrix, wherein the horizontal anomalies each comprise a clustering of at least two objects of the plurality of objects into a common cluster based on a first information source of the plurality of different information sources and simultaneously clustering the at least two objects of the plurality of objects into different clusters based on a second information source of the plurality of different information sources, wherein the steps of receiving the descriptions, generating the similarity matrix, and identifying the at least one horizontal anomalies are performed using a computer system, and wherein combining the information sources comprises; placing each individual similarity matrix along a block diagonal of the similarity matrix; and filling off-diagonal entries of the similarity matrix using weighted identity matrices, wherein a weight of the weighted identity matrices is a constraint on relationships across the plurality of information sources.