13. The horizontal anomaly detection method of claim 11 , wherein quantifying the difference between clustering solutions of each similarity matrices to identify scores that signify the clustering of at least two objects of the N objects into a common cluster based on the first information source of the plurality of different information sources and simultaneously clustering the at least two objects of the N objects into different clusters based on the second information source of the plurality of different information sources constitutes identifying at least one horizontal anomaly.
11. A horizontal anomaly detection method comprising: determining a plurality of N×N similarity matrices for N objects, wherein entries of each similarity matrix define similarities between different pairs of the N objects from a plurality of different information sources; performing a soft clustering on the similarity matrices together with a constraint that an object should be similarly assigned to a same cluster in each of the similarity matrices; and quantifying a difference between clustering solutions of each similarity matrices to identify scores that signify a clustering of at least two objects of the N 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 N objects into different clusters based on a second information source of the plurality of different information sources, 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.