P-N-RMiner: A generic framework for mining interesting structured relational patterns

Abstract

Abstract-Local pattern mining methods are fragmented along two dimensions: the pattern syntax, and the data types on which they are applicable. Pattern syntaxes considered in the literature include subgroups, n-sets, itemsets, and many more; common data types include binary, categorical, and real-valued. Recent research on pattern mining in relational databases has shown how the aforementioned pattern syntaxes can be unified in a single framework. However, a unified understanding of how to deal with various data types is lacking, certainly for more complexly structured types such as time of day (which is circular), geographical location, terms from a taxonomy, etc.In this paper, we introduce a generic approach for mining interesting local patterns in (relational) data involving such structured data types as attributes. Importantly, we show how this can be done in a generic manner, by modelling the structure within a set of attribute values as a partial order. We then derive a measure of subjective interestingness of such patterns using Information Theory, and propose an algorithm for effectively enumerating all patterns of this syntax. Through empirical evaluation, we found that (a) the new interestingness derivation is relevant and cannot be approximated using existing tools, (b) the new tool, P-N-RMiner, finds patterns that are substantially more informative, and (c) the new enumeration algorithm is considerably faster.

Subjectively Interesting Subgroup Discovery on Real-Valued Targets

“…We base our approach on the more recent and systematic approach named FORSIED [2], [3]. This framework has been applied successfully to a variety of data mining problems, such as mining relational patterns [24], community detection [25], clustering [26], and dimensionality reduction [27]. Maximum Entropy modeling for real-valued data has also been studied before [28], in order to compute the significance of the Weighted Relative Accuracy in SD.…”

Discovering subjectively interesting multigraph patterns

“…Arguably, the most fundamental contribution in the context of the latter has been made by De Bie (2011), where a generic framework based on maximum entropy principle was proposed to allow modelling of prior beliefs, laying the basis for subjective interestingness. Lijffijt et al (2016) defined subjective interestingness for structured n-ary relational patterns. In this, it is assumed that the prior belief on the number of entities of a specific type to which a given entity is related is known.…”

SIAS-miner: mining subjectively interesting attributed subgraphs

“…A similar statement holds for intervals. P-N-RMiner [15] is an extension of RMiner that exploits such hierarchies for efficiency. Firstly, fewer connections (edges) are needed between the entity types hence there is a smaller memory requirement.…”

“…Hence, we only use it to enumerate all candidates. The computational complexity is clearly exponential as the number of outputs may be exponential in the size of the input, plus in this setting no fixpointenumeration-based algorithm may have polynomial-time delay [15]. Scalability experiments are presented in Sec.…”

“…The measure is based on the gain from a Maximum Entropy background model that delineates the current knowledge of a user, hence it is subjective, i.e., particular to the modeled belief state. P-N-RMiner [15], the tool used here for pattern enumeration, has also been developed under FORSIED. However, the interestingness measure in this paper is very different, because the information contained in the patterns shown to the user does not align with the output of P-N-RMiner.…”