Integer Linear Programming for Pattern Set Mining; with an Application to Tiling

Ouali, Abdelkader; Zimmermann, Albrecht; Loudni, Samir; Lebbah, Yahia; Crémilleux, Bruno; Boizumault, Patrice; Loukil, Lakhdar

doi:10.1007/978-3-319-57529-2_23

Cited by 7 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…contain patterns very similar to other patterns. There are various pattern set selection ways of reducing size and redundancy of a pattern set [6,19,29]. In our experiments we will use the 𝑔𝛽 pattern set selection algorithm first defined and applied to core closed patterns in [25].…”

Section: Exhibiting Patterns Of Interestmentioning

confidence: 99%

Exploring and mining attributed sequences of interactions

Viard¹,

Soldano²,

Santini³

2021

Preprint

View full text Add to dashboard Cite

We are faced with data comprised of entities interacting over time: this can be individuals meeting, customers buying products, machines exchanging packets on the IP network, among others. Capturing the dynamics as well as the structure of these interactions is of crucial importance for analysis. These interactions can almost always be labeled with content: group belonging, reviews of products, abstracts, etc. We model these stream of interactions as stream graphs, a recent framework to model interactions over time. Formal Concept Analysis provides a framework for analyzing concepts evolving within a context. Considering graphs as the context, it has recently been applied to perform closed pattern mining on social graphs. In this paper, we are interested in pattern mining in sequences of interactions. After recalling and extending notions from formal concept analysis on graphs to stream graphs, we introduce algorithms to enumerate closed patterns on a labeled stream graph, and introduce a way to select relevant closed patterns. We run experiments on two real-world datasets of interactions among students and citations between authors, and show both the feasibility and the relevance of our method.

show abstract

Section: Exhibiting Patterns Of Interestmentioning

confidence: 99%

Exploring and mining attributed sequences of interactions

Viard¹,

Soldano²,

Santini³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Our approach provides a framework that allows incorporating ideas from various pattern set mining and pattern selection approaches, designed to create smaller, compressed or userpreferred set of patterns, into redescription set mining and exploration process. Pattern set mining approaches, such as Xin et al 2005 2017can be used to construct sets of new redescriptions describing the selected subsets of entities/attributes of interest, whereas pattern selection methods (Xin et al 2005;Knobbe and Ho 2006;Pei et al 2007;Ouali et al 2017;Kalofolias et al 2016;Mihelčić et al 2017a) can be used to perform automated and fine-grained selection of redescriptions from a selected subset of redescriptions. Techniques for performing redescription mining with entity/attribute constraints already exist (Zaki and Ramakrishnan 2005;Mihelčić et al 2017b).…”

Section: Specifics and Motivation For Developing Intersetmentioning

confidence: 99%

Targeted and contextual redescription set exploration

Mihelčić

Šmuc

2018

Mach Learn

View full text Add to dashboard Cite

One important problem occurring in redescription mining is a very large number of produced redescriptions. This makes analyses time consuming and generally difficult. We present the targeted and contextual redescription set exploration, realized through the tool InterSet. The main purpose of the tool is to derive additional knowledge from the redescription set which allows exploring parts of redescription set of interest and examining redescriptions individually or in the broader context, with the aim of increasing overall understandability. InterSet allows relating, grouping redescriptions, observing distributions of various redescription properties and selecting the appropriate subsets for further, detailed study. This allows gaining knowledge about the underlying data, help in forming, understanding, supporting research hypothesis or assists in understanding one or more redescriptions of interest. The tool provides three different, fully connected interaction modes based on: (1) similarity of entity occurrence in redescription support sets, (2) attribute co-occurrence in redescriptions and (3) redescription quality measures. Additionally, it allows exploration of relations between different redescriptions by creating a graph visualization that includes the top k-shortest paths containing selected redescriptions. On the individual redescription level, it allows studying value distributions of described entities, for a given set of attributes.

show abstract

“…Such methods aim to find the best possible solution. The pioneering work of [40] proposes a level wise algorithm to explore the search space, and [21], [26], [38] use a Constraint Programming (CP) solver to retrieve the k-pattern set. In large and complex datasets, the exhaustive selection of the best k-pattern set becomes unfeasible, even if pruning strategies are used.…”

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

Integer Linear Programming for Pattern Set Mining; with an Application to Tiling

Cited by 7 publications

References 15 publications

Exploring and mining attributed sequences of interactions

Exploring and mining attributed sequences of interactions

Targeted and contextual redescription set exploration

FSSD - A Fast and Efficient Algorithm for Subgroup Set Discovery

Contact Info

Product

Resources

About