Lossless compression replaces redundant patterns of data in a file with codes that take up less space.
We set forth to extract only frequent patterns which are non-redundant, but in the process of finding frequent non-redundant patterns, we might generate also some redundant patterns.
Hence, the importance of all redundant patterns that represent a certain substructure is fused in the coloring of a molecule.
The SVM might split the importance (weight) of a substructure among the redundant patterns that represent it.
Though it has wide range of application domains, existing algorithms failed to mine closed flexible patterns without generating any false positive, i.e. non-closed and ∕or redundant patterns.
Experiments show that the proposed algorithm is efficient to produce longer – non redundant – patterns, and that the new data representation is efficient for both computational time and memory usage.
While these are useful tools, interpretation of obtained results may be rather challenging for users, as the outcome may involve, e.g., large sets of subspace clusters, many of which contain redundant patterns, or patterns that are not relevant for a specific analysis goal.
