A framework for mining interesting pattern sets

Bie, Tijl De; Kontonasios, Kleanthis-Nikolaos; Spyropoulou, Eirini

doi:10.1145/1816112.1816117

Cited by 13 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To compute the interestingness of ↵-CCSs, the general framework from [5], [3] provides a justification for the quantification of the interestingness of a pattern as the ratio of its information content and its description length. Let us discuss these two components in turn.…”

Section: Interestingnessmentioning

confidence: 99%

See 1 more Smart Citation

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

Section: Interestingnessmentioning

confidence: 99%

“…The smaller the probability the pattern has under this so-called background distribution, the more information the pattern conveys to the user. This amount of subjective information is what the information content aims to quantify (see [5], [3] for details).…”

Section: A Information Contentmentioning

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

show abstract

“…(3) Two-step approach. These algorithms start by (a) generating a collection of local patterns by some exhaustive or heuristic technique followed by (b) a heuristic selection of a smaller subset of complementary patterns [5], [8], [44]. However, since the number of local patterns can be large, these algorithms need a huge amount of memory to store them.…”

Section: Introductionmentioning

confidence: 99%

FSSD - A Fast and Efficient Algorithm for Subgroup Set Discovery

Belfodil

Bendimerad

et al. 2019

2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

View full text Add to dashboard Cite

Subgroup discovery (SD) is the task of discovering interpretable patterns in the data that stand out w.r.t. some property of interest. Discovering patterns that accurately discriminate a class from the others is one of the most common SD tasks. Standard approaches of the literature are based on local pattern discovery, which is known to provide an overwhelmingly large number of redundant patterns. To solve this issue, pattern set mining has been proposed: instead of evaluating the quality of patterns separately, one should consider the quality of a pattern set as a whole. The goal is to provide a small pattern set that is diverse and well-discriminant to the target class. In this work, we introduce a novel formulation of the task of diverse subgroup set discovery where both discriminative power and diversity of the subgroup set are incorporated in the same quality measure. We propose an efficient and parameter-free algorithm dubbed FSSD and based on a greedy scheme. FSSD uses several optimization strategies that enable to efficiently provide a high quality pattern set in a short amount of time.

show abstract

“…The user interest is defined through a set of features that can appear on the left and right side of the discovered rules and by thresholds on selected interest measures. A typical task produces many rules that formally match these criteria, but only few are interesting to the user [1]. The uninteresting rules can be filtered using domain knowledge; the challenge is to balance the investment of user's time to provide the required input with the utility gained from the filtered mining result.…”

Section: Introductionmentioning

confidence: 99%

Association Rule Mining Following the Web Search Paradigm

Škrabal

Šimůnek

Vojíř

et al. 2012

Machine Learning and Knowledge Discovery in Databases

View full text Add to dashboard Cite

I:ZI Miner (sewebar.vse.cz/izi-miner) is an association rule mining system with a user interface resembling a search engine. It brings to the web the notion of interactive pattern mining introduced by the MIME framework at ECML'11 and KDD'11. In comparison with MIME, I:ZI Miner discovers multi-valued attributes, supports the full range of logical connectives and 19 interest measures. A relevance feedback module is used to filter the rules based on previous user interactions.

show abstract

A framework for mining interesting pattern sets

Cited by 13 publications

References 17 publications

Mining approximate multi-relational patterns

Mining approximate multi-relational patterns

FSSD - A Fast and Efficient Algorithm for Subgroup Set Discovery

Association Rule Mining Following the Web Search Paradigm

Contact Info

Product

Resources

About