Interesting Multi-relational Patterns

Spyropoulou, Eirini; Bie, Tijl De

doi:10.1109/icdm.2011.82

Cited by 24 publications

(12 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A graph representation of such a multi-relational database takes the form of a K-partite graph [5], if there are no relations between homogeneous entities or equivalently, if there are no edges between vertices that belong to the same partite set. In practice, such relationships are not rare.…”

Section: Multi-relational Graphsmentioning

confidence: 99%

See 1 more Smart Citation

Fast search for dynamic multi-relational graphs

Choudhury

Holder

Feo

et al. 2013

Proceedings of the Workshop on Dynamic Networks Management and Mining

View full text Add to dashboard Cite

Acting on time-critical events by processing ever growing social media or news streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Continuous queries or techniques to search for rare events that typically arise in monitoring applications have been studied extensively for relational databases. This work is dedicated to answer the question that emerges naturally: how can we efficiently execute a continuous query on a dynamic graph? This paper presents an exact subgraph search algorithm that exploits the temporal characteristics of representative queries for online news or social media monitoring. The algorithm is based on a novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the structural and semantic characteristics of the underlying multi-relational graph. The paper concludes with extensive experimentation on several real-world datasets that demonstrates the validity of this approach.

show abstract

Section: Multi-relational Graphsmentioning

confidence: 99%

“…Second, all these queries aim to discover a number of temporal events that share the same context, such as a common set of keywords and location. Lastly, a multi-relational graph often takes the form of k-partite graphs [4,5] where each partite set represents a group of entities of the same type. For queries as ones described in Fig.…”

Section: Introductionmentioning

confidence: 99%

Fast search for dynamic multi-relational graphs

Choudhury

Holder

Feo

et al. 2013

Proceedings of the Workshop on Dynamic Networks Management and Mining

View full text Add to dashboard Cite

show abstract

“…The MaxEnt approach and the information ratio have recently been extended in the context of relational databases36. The method is based on a graph representation of the relational database which makes the application of MaxEnt modeling straightforward.…”

Section: Unexpectedness For Other Kinds Of Patternsmentioning

confidence: 99%

Knowledge discovery interestingness measures based on unexpectedness

Kontonasios

Spyropoulou

Bie

2012

WIREs Data Min & Knowl

Self Cite

View full text Add to dashboard Cite

Knowledge discovery methods often discover a large number of patterns. Although this can be considered of interest, it certainly presents considerable challenges too. Indeed, this set of patterns often contains lots of uninteresting patterns that risk overwhelming the data miner. In addition, a single interesting pattern can be discovered in a multitude of tiny variations that for all practical purposes are redundant. These issues are referred to as the pattern explosion problem. They lie at the basis of much recent research attempting to quantify interestingness and redundancy between patterns, with the purpose of filtering down a large pattern set to an interesting and compact subset. Many diverse approaches to interestingness and corresponding interestingness measures (IMs) have been proposed in the literature. Some of them, named objective IMs, define interestingness only based on objective criteria of the pattern and data at hand. Subjective IMs additionally depend on the user's prior knowledge about the dataset. Formalizing unexpectedness is probably the most common approach for defining subjective IMs, where a pattern is deemed unexpected if it contradicts the user's expectations about the dataset. Such subjective IMs based on unexpectedness form the focus of this paper. We categorize measures based on unexpectedness into two major subgroups, namely, syntactical and probabilistic approaches. Based on this distinction, we survey different methods for assessing the unexpectedness of patterns with a special focus on frequent itemsets,

show abstract

“…Here we follow the same approach as in [13], [14], but it should be noted that other prior beliefs and resulting background distributions may be more appropriate, depending on the situation.…”

Section: A Information Contentmentioning

confidence: 99%

“…To address this need, we recently proposed the pattern syntax of Maximal Complete Connected Subsets (MCCSs) [13], [14], [15], which enables mining such kind of patterns from This work was done while Eirini Spyropoulou was a PhD student at the University of Bristol. multi-relational data.…”

Section: Introductionmentioning

confidence: 99%

Mining approximate multi-relational patterns

Spyropoulou

Bie

2014

2014 International Conference on Data Science and Advanced Analytics (DSAA)

Self Cite

View full text Add to dashboard Cite

Abstract-Three recent trends aim to make local pattern mining more directly suited for use on data as it presents itself in practice, namely in a multi-relational form and affected by noise. The first of these trends is the generalisation of local pattern syntaxes to approximate, noise-tolerant, variants (notably faulttolerant itemset mining and community detection). The second of these trends is to develop pattern syntaxes that are directly applicable to multi-relational data. The third one is to better quantify the interestingness of and redundancy between such local patterns.In this paper we leverage recent results from these lines of research to introduce a noise-tolerant pattern syntax for multirelational data. We show how enumerating all patterns of this syntax in a given database can be done remarkably efficiently. We contribute a way to quantify the interestingness of these patterns, thus overcoming the pattern explosion problem. And finally, we show the usefulness of the pattern syntax and the scalability of the algorithm by presenting experimental results on real world and synthetic data.

show abstract

Interesting Multi-relational Patterns

Cited by 24 publications

References 22 publications

Fast search for dynamic multi-relational graphs

Fast search for dynamic multi-relational graphs

Knowledge discovery interestingness measures based on unexpectedness

Mining approximate multi-relational patterns

Contact Info

Product

Resources

About