Turning Krimp into a Triclustering Technique on Sets of Attribute-Condition Pairs that Compress
Mining ternary relations or triadic Boolean tensors is one of the recent trends in knowledge discovery that allows one to take into account various modalities of input object-attribute data. For example, in movie databases like IMBD, an analyst may find not only movies grouped by specific genres but see their common keywords. In the so-called folksonomies, users can be grouped according to their shared resources and used tags. In gene expression analysis, genes can be grouped along with samples of tissues and time intervals providing comprehensible patterns. However, pattern explosion effects even with one more dimension are seriously aggravated. In this paper, we continue our previous study on searching for a smaller collection of “optimal” patterns in triadic data with respect to a set of quality criteria such as patterns’ cardinality, density, diversity, coverage, etc. We show how a simple data preprocessing has enabled us to use the frequent itemset mining algorithm Krimp based on MDL-principle for triclustering purposes.