Interesting pattern mining in multi-relational data

Spyropoulou, Eirini; Bie, Tijl De; Boley, Mario

doi:10.1007/s10618-013-0319-9

Cited by 38 publications

(83 citation statements)

References 52 publications

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“…In the following section, we argue that the background distribution can be fitted in the exact same way as in [24]. However, how to compute the probability that a given pattern is present-and thus its self-information-is not trivial.…”

Section: Information Contentmentioning

confidence: 99%

“…In short, we add a properness constraint and the pattern syntax is otherwise equivalent to [23,24]. Our implementation and theory also support n-ary relationships, but we do not discuss this further in order to prevent unnecessary complications in the exposition.…”

Section: Definition 4 An Entity Set F ⊆ E Is Maximal Iffmentioning

confidence: 99%

“…In [24], this framework is used successfully to formalise the interestingness of Complete Connected Subsets (CCSs), without the properness requirement that lies at the core of the contributions in this paper. The properness requirement creates an opportunity as well as a non-trivial challenge.…”

Section: General Approachmentioning

confidence: 99%

“…Local pattern mining has traditionally been rooted in categorical or even binary data, including algorithms for frequent itemset mining and variants [2], n-set mining [7], subgroup discovery [14], and multi-relational pattern mining [15,24]. Some of these local pattern mining approaches have been extended in various ways to include ordinal, real-valued, or other data structures.…”

Section: Introductionmentioning

confidence: 99%

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

Lijffijt

Spyropoulou

Kang

et al. 2016

Int J Data Sci Anal

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Methods for local pattern mining are fragmented along two dimensions: the pattern syntax, and the data types on which they are applicable. Pattern syntaxes include subgroups, n-sets, itemsets, and many more; common data types include binary, categorical, and real-valued. Recent research on relational pattern mining has shown how the aforementioned pattern syntaxes can be unified in a single framework. However, a unified model to deal with various data types is lacking, certainly for more complexly structured types such as real numbers, time of day-which is circular-, geographical location, terms from a taxonomy, etc. We introduce P-N-RMiner, a generic tool for mining interesting local patterns in (relational) data with structured attributes. We show how to handle the attribute structures in a generic manner, by modelling them as partial orders. We also derive an information-theoretic subjective interestingness measure for such patterns and present an algorithm to efficiently enumerate the patterns. We find that (1) P-N-RMiner finds patterns that are substantially more informative, (2) the new interestingness measure cannot be approximated using existing methods, and (3) we can leverage the partial orders to speed up enumeration.

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Section: Information Contentmentioning

confidence: 99%

Section: Definition 4 An Entity Set F ⊆ E Is Maximal Iffmentioning

confidence: 99%

Section: General Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Lijffijt

Spyropoulou

Kang

et al. 2016

Int J Data Sci Anal

Self Cite

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show abstract

“…A disadvantage is that the exact implementation of the 'MaxEnt approach' heavily relies on the specific data and pattern types at hand, but instances have been proposed for a variety of data types, e.g. for binary data [30] and multi-relational-data [31].…”

Section: Formalising Subjective Interestingnessmentioning

confidence: 99%

Interactive Data Exploration Using Pattern Mining

Leeuwen

2014

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

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Abstract. We live in the era of data and need tools to discover valuable information in large amounts of data. The goal of exploratory data mining is to provide as much insight in given data as possible. Within this field, pattern set mining aims at revealing structure in the form of sets of patterns. Although pattern set mining has shown to be an effective solution to the infamous pattern explosion, important challenges remain. One of the key challenges is to develop principled methods that allow user-and task-specific information to be taken into account, by directly involving the user in the discovery process. This way, the resulting patterns will be more relevant and interesting to the user. To achieve this, pattern mining algorithms will need to be combined with techniques from both visualisation and human-computer interaction. Another challenge is to establish techniques that perform well under constrained resources, as existing methods are usually computationally intensive. Consequently, they are only applied to relatively small datasets and on fast computers. The ultimate goal is to make pattern mining practically more useful, by enabling the user to interactively explore the data and identify interesting structure. In this paper we describe the state-of-the-art, discuss open problems, and outline promising future directions.

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