The Minimum Description Length Principle for Pattern Mining: A Survey

This is about the Minimum Description Length (MDL) principle applied to pattern mining. The length of this description is kept to the minimum. Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The MDL principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, as well as of work on the theory behind the MDL and similar principles, we review MDL-based methods for mining various types of data and patterns. Finally, we open a discussion on some issues regarding these methods, and highlight currently active related data analysis problems

Phrase table pruning for Statistical Machine Translation

Phrase-Based Statistical Machine Translation systems model the translation process using pairs of corresponding sequences of words extracted from parallel corpora. These biphrases are stored in phrase tables that typically contain several millions such entries, making it di cult to assess their quality without going to the end of the translation process. Our work is based on the examplifying study of phrase tables generated from the Europarl data, from French to English. We give some statistical information about the biphrases contained in the phrase table, evaluate the coverage of previously unseen sentences and analyse the e ects of pruning on the translation

Maximizing the Diversity of Exposure in a Social Network

Social-media platforms have created new ways for citizens to stay informed and participate in public debates. However, to enable a healthy environment for information sharing, social deliberation, and opinion formation, citizens need to be exposed to sufficiently diverse viewpoints that challenge their assumptions, instead of being trapped inside filter bubbles. In this paper, we take a step in this direction and propose a novel approach to maximize the diversity of exposure in a social network. We formulate the problem in the context of information propagation, as a task of recommending a small number of news articles to selected users. We propose a realistic setting where we take into account content and user leanings, and the probability of further sharing an article. This setting allows us to capture the balance between maximizing the spread of information and ensuring the exposure of users to diverse viewpoints. The resulting problem can be cast as maximizing a monotone and submodular function subject to a matroid constraint on the allocation of articles to users. It is a challenging generalization of the influence maximization problem. Yet, we are able to devise scalable approximation algorithms by introducing a novel extension to the notion of random reverse-reachable sets. We experimentally demonstrate the efficiency and scalability of our algorithm on several real-world datasets

Finding relational redescriptions

We introduce relational redescription mining, that is, the task of finding two structurally different patterns that describe nearly the same set of object pairs in a relational dataset. By extending redescription mining beyond propositional and real-valued attributes, it provides a powerful tool to match different relational descriptions of the same concept. We propose an alternating scheme for solving this problem. Its core consists of a novel relational query miner that efficiently identifies discriminative connection patterns between pairs of objects. Compared to a baseline Inductive Logic Programming (ILP) approach, our query miner is able to mine more complex queries, much faster. We performed extensive experiments on three real world relational datasets, and present examples of redescriptions found, exhibiting the power of the method to expressively capture relations present in these networks

Menetelmiä jälleenkuvausten louhintaan

In scientific investigations data oftentimes have different nature. For instance, they might originate from distinct sources or be cast over separate terminologies. In order to gain insight into the phenomenon of interest, a natural task is to identify the correspondences that exist between these different aspects. This is the motivating idea of redescription mining, the data analysis task studied in this thesis. Redescription mining aims to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. A practical example in biology consists in finding geographical areas that admit two characterizations, one in terms of their climatic profile and one in terms of the occupying species. Discovering such redescriptions can contribute to better our understanding of the influence of climate over species distribution. Besides biology, applications of redescription mining can be envisaged in medicine or sociology, among other fields. Previously, redescription mining was restricted to propositional queries over Boolean attributes. However, many conditions, like aforementioned climate, cannot be expressed naturally in this limited formalism. In this thesis, we consider more general query languages and propose algorithms to find the corresponding redescriptions, making the task relevant to a broader range of domains and problems. Specifically, we start by extending redescription mining to non-Boolean attributes. In other words, we propose an algorithm to handle nominal and real-valued attributes natively. We then extend redescription mining to the relational setting, where the aim is to find corresponding connection patterns that relate almost the same object tuples in a network. We also study approaches for selecting high quality redescriptions to be output by the mining process. The first approach relies on an interface for mining and visualizing redescriptions interactively and allows the analyst to tailor the selection of results to meet his needs. The second approach, rooted in information theory, is a compression-based method for mining small sets of associations from two-view datasets. In summary, we take redescription mining outside the Boolean world and show its potential as a powerful exploratory method relevant in a broad range of domains.Tieteellinen tutkimusaineisto kootaan usein eri termistöä käyttävistä lähteistä. Näiden erilaisten näkökulmienvälisten vastaavuuksien ja yhteyksien tunnistaminen on luonnollinen tapa lähestyä tutkittavaa ilmiötä. Väitöskirjassa tarkastellaan juuri tähän pyrkivää data-analyysimenetelmää, jälleenkuvausten louhintaa (redescription mining). Jälleenkuvausten tavoitteena on yhtäältä kuvata samaa asiaa vaihoehtoisilla tavoilla ja toisaalta tunnistaa sellaiset asiat, joilla on useita eri kuvauksia. Jälleenkuvausten louhinnalla on mahdollisia sovelluksia mm. biologiassa, lääketieteessä ja sosiologiassa. Biologiassa voidaan esimerkiksi etsiä sellaisia maantieteellisiä alueita, joita voidaan luonnehtia kahdella vaihtoehtoisella tavalla: joko kuvaamalla alueen ilmasto tai kuvaamalla alueella elävät lajit. Esimerkiksi Skandinaviassa ja Baltiassa on ensinnäkin samankaltaiset lämpötila- ja sadeolosuhteet ja toisekseen hirvi on yhteinen laji molemmilla alueilla. Tällaisten jälleenkuvausten löytäminen voi auttaa ymmärtämään ilmaston vaikutuksia lajien levinneisyyteen. Lääketieteessä taas jälleenkuvauksilla voidaan löytää potilaiden taustatietojen sekä heidän oireidensa ja diagnoosiensa välisiä yhteyksiä, joiden avulla taas voidaan mahdollisesti paremmin ymmärtää itse sairauksia. Aiemmin jälleenkuvausten louhinnassa on rajoituttu tarkastelemaan totuusarvoisia muuttujia sekä propositionaalisia kuvauksia. Monia asioita, esimerkiksi ilmastotyyppiä, ei kuitenkaan voi luontevasti kuvata tällaisilla rajoittuneilla formalismeilla. Väitöskirjatyössä laajennetaankin jälleenkuvausten käytettävyyttä. Työssä esitetään ensimmäinen algoritmi jälleenkuvausten löytämiseen aineistoista, joissa attribuutit ovat reaalilukuarvoisia ja käsitellään ensimmäistä kertaa jälleenkuvausten etsintää relationaalisista aineistoista, joissa asiat viittaavat toisiinsa. Lisäksi väitöskirjassa tarkastellaan menetelmiä, joilla jälleenkuvausten joukosta voidaan valita kaikkein laadukkaimmat. Näihin menetelmiin kuuluvat sekä interaktiivinen käyttöliittymä jälleenkuvausten louhintaan ja visualisointiin, että informaatioteoriaan perustuvaa parametriton menetelmä parhaiden kuvausten valitsemiseksi. Kokonaisuutena väitöskirjatyössä siis laajennetaan jälleenkuvausten louhintaa totuusarvoisista muuttujista myös muunlaisten aineistojen käsittelyyn sekä osoitetaan menetelmän mahdollisuuksia monenlaisilla sovellusalueilla.Méthodes pour la fouille de redescriptions Lors de l'analyse scientifique d'un phénomène, les données disponibles sont souvent de différentes natures. Entre autres, elles peuvent provenir de différentes sources ou utiliser différentes terminologies. Découvrir des correspondances entre ces différents aspects fournit un moyen naturel de mieux comprendre le phénomène à l'étude. C'est l'idée directrice de la fouille de redescriptions (redescription mining), la méthode d'analyse de données étudiée dans cette thèse. La fouille de redescriptions a pour but de trouver diverses manières de décrire les même choses et vice versa, de trouver des choses qui ont plusieurs descriptions en commun. Un exemple en biologie consiste à déterminer des zones géographiques qui peuvent être caractérisées de deux manières, en terme de leurs conditions climatiques d'une part, et en terme des espèces animales qui y vivent d'autre part. Les régions européennes de la Scandinavie et de la Baltique, par exemple, ont des conditions de températures et de précipitations similaires et l'élan est une espèce commune aux deux régions. Identifier de telles redescriptions peut potentiellement aider à élucider l'influence du climat sur la distribution des espèces animales. Pour prendre un autre exemple, la fouille de redescriptions pourrait être appliquée en médecine, pour mettre en relation les antécédents des patients, leurs symptômes et leur diagnostic, dans le but d'améliorer notre compréhension des maladies. Auparavant, la fouille de redescriptions n'utilisait que des requêtes propositionnelles à variables booléennes. Cependant, de nombreuses conditions, telles que le climat cité ci-dessus, ne peuvent être exprimées dans ce formalisme restreint. Dans cette thèse, nous proposons un algorithme pour construire directement des redescriptions avec des variables réelles. Nous introduisons ensuite des redescriptions mettant en jeu des liens entre les objets, c'est à dire basées sur des requêtes relationnelles. Nous étudions aussi des approches pour sélectionner des redescriptions de qualité, soit en utilisant une interface permettant la fouille et la visualisation interactives des redescriptions, soit via une méthode sans paramètres motivée par des principes de la théorie de l'information. En résumé, nous étendons la fouille de redescriptions hors du monde booléen et montrons qu'elle constitue une méthode d'exploration de données puissante et pertinente dans une large variété de domaines

Redescription Mining: An Overview.

International audienceIn many real-world data analysis tasks, we have different types of data over the same objects or entities, perhaps because the data originate from distinct sources or are based on different terminologies. In order to understand such data, an intuitive approach is to identify thecorrespondences that exist between these different aspects. This isthe motivating principle behind redescription mining, a data analysistask that aims at finding distinct commoncharacterizations of the same objects.This paper provides a short overview of redescription mining; what it is and how it is connected to other data analysis methods; the basic principles behind current algorithms for redescription mining; and examples and applications of redescription mining for real-world data analysis problems

Analysing Political Opinions Using Redescription Mining

International audienceUnderstanding the socio-economical background of voters supporting a certain cause or, vice versa, understanding the political stance of people from a certain socio-economical niche are important questions in political sciences. Traditionally, answering these questions has required the researcher to fix either the political stance or the socio-economical background. In this paper, we propose using redescription mining to automatically find the stances and niches that correspond to each other. We show how redescription mining can be applied to open data from voting advice applications, providing insights about the position of the candidates to parliamentary elections. Furthermore, we show that these insights are not only descriptive, but that they also generalize well to new data

Association Discovery in Two-View Data

International audienceTwo-view datasets are datasets whose attributes are naturally split into two sets, each providing a different view on the same set of objects. We introduce the task of finding small and non-redundant sets of associations that describe how the two views are related. To achieve this, we propose a novel approach in which sets of rules are used to translate one view to the other and vice versa. Our models, dubbed translation tables, contain both unidirectional and bidirectional rules that span both views and provide lossless translation from either of the views to the opposite view. To be able to evaluate different translation tables and perform model selection, we present a score based on the Minimum Description Length (MDL) principle. Next, we introduce three TRANSLATOR algorithms to find good models according to this score. The first algorithm is parameter-free and iteratively adds the rule that improves compression most. The other two algorithms use heuristics to achieve better trade-offs between runtime and compression. The empirical evaluation on real-world data demonstrates that only modest numbers of associations are needed to characterize the two-view structure present in the data, while the obtained translation rules are easily interpretable and provide insight into the data

From Sets of Good Redescriptions to Good Sets of Redescriptions

International audienceRedescription mining aims at finding pairs of queries over data variables that describe roughly the same set of observations. These redescriptions can be used to obtain different views on the same set of entities. So far, redescription mining methods have aimed at listing all redescriptions supported by the data. Such an approach can result in many redundant redescriptions and hinder the user's ability to understand the overall characteristics of the data. In this work, we present an approach to find a good set of redescriptions, instead of finding a set of good redescriptions. That is, we present a way to remove the redundant redescriptions from a given set of redescriptions. We measure the redundancy using a framework inspired by the subjective interestingness based on maximum-entropy distributions as proposed by De Bie in 2011. Redescriptions, however, raise their unique requirements on the framework, and our solution differs significantly from the existing ones. Notably, our approach can handle disjunctions and conjunctions in the queries, whereas the existing approaches are limited only to conjunctive queries. The framework also reduces the redundancy in the redescription mining results, as we show in our empirical evaluation

Top-k overlapping densest subgraphs

International audienceFinding dense subgraphs is an important problem in graph mining and has many practical applications. At the same time, while large real-world networks are known to have many communities that are not well-separated, the majority of the existing work focuses on the problem of finding a single densest subgraph. Hence, it is natural to consider the question of finding the top-kdensest subgraphs. One major challenge in addressing this question is how to handle overlaps: eliminating overlaps completely is one option, but this may lead to extracting subgraphs not as dense as it would be possible by allowing a limited amount of overlap. Furthermore, overlaps are desirable as in most real-world graphs there are vertices that belong to more than one community, and thus, to more than one densest subgraph. In this paper we study the problem of finding top-koverlapping densest subgraphs, and we present a new approach that improves over the existing techniques, both in theory and practice. First, we reformulate the problem definition in a way that we are able to obtain an algorithm with constant-factor approximation guarantee. Our approach relies on using techniques for solving the max-sum diversification problem, which however, we need to extend in order to make them applicable to our setting. Second, we evaluate our algorithm on a collection of benchmark datasets and show that it convincingly outperforms the previous methods, both in terms of quality and efficiency