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 different similarity relationship between the plurality of objects, thereby establishing a plurality of different similarity relationships, wherein each of the plurality of different similarity relationships represents a unique set of criteria by which the plurality of objects may be grouped as similar and wherein each of the plurality of different similarity relationships are represented by an individual similarity matrix; 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 in accordance with each of the plurality of different similarity relationships and wherein the generating of the similarity matrix comprises placing, in the similarity matrix, 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; and identifying at least one horizontal anomaly within the plurality of objects from the similarity matrix, wherein the horizontal anomalies each comprise a first 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 performing a second clustering of 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 an expectation exists that, in the first clustering, objects of the common cluster based on the first information source would also be clustered together in the second clustering based on the second information source, 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.