Temporal and Spatial Data Mining with Second-Order Hidden Models

In the frame of designing a knowledge discovery system, we have developed stochastic models based on high-order hidden Markov models. These models are capable to map sequences of data into a Markov chain in which the transitions between the states depend on the \texttt{n} previous states according to the order of the model. We study the process of achieving information extraction fromspatial and temporal data by means of an unsupervised classification. We use therefore a French national database related to the land use of a region, named Teruti, which describes the land use both in the spatial and temporal domain. Land-use categories (wheat, corn, forest, ...) are logged every year on each site regularly spaced in the region. They constitute a temporal sequence of images in which we look for spatial and temporal dependencies. The temporal segmentation of the data is done by means of a second-order Hidden Markov Model (\hmmd) that appears to have very good capabilities to locate stationary segments, as shown in our previous work in speech recognition. Thespatial classification is performed by defining a fractal scanning ofthe images with the help of a Hilbert-Peano curve that introduces atotal order on the sites, preserving the relation ofneighborhood between the sites. We show that the \hmmd performs aclassification that is meaningful for the agronomists.Spatial and temporal classification may be achieved simultaneously by means of a 2 levels \hmmd that measures the \aposteriori probability to map a temporal sequence of images onto a set of hidden classes

Mining Complex Hydrobiological Data with Galois Lattices

We have used Galois lattices for mining hydrobiological data. These data are about macrophytes, that are macroscopic plants living in water bodies. These plants are characterized by several biological traits, that own several modalities. Our aim is to cluster the plants according to their common traits and modalities and to find out the relations between traits. Galois lattices are efficient methods for such an aim, but apply on binary data. In this article, we detail a few approaches we used to transform complex hydrobiological data into binary data and compare the first results obtained thanks to Galois lattices

SIXIÈME ATELIER : Représentation et raisonnement sur le temps et l'espace (RTE 2011)

Actes de l'atelier RTE 2011, Plate-forme AFIA, ChambéryNational audienceLa représentation du temps et de l'espace ainsi que les modèles de raisonnements associés sont des thèmes largement étudiés en informatique, d'une manière générale, et en intelligence artificielle, en particulier. Ces thèmes sont de plus en plus importants dans de nombreux domaines de notre société, en particulier là où est disponible une très grande quantité d'informations et de services évoluant au cours du temps ou dans l'espace. Les techniques temporelles et/ou spatiales sont, par exemple, importantes dans : la gestion des grandes quantités de données, l'analyse et la fouille de ces données, la simulation et l'analyse de l'évolution temporelle de processus, l'évaluation de la sécurité et la sûreté, la gestion dynamique des connaissances, la gestion de l'espace, la prévention des risques naturels, la modélisation des systèmes dynamiques et complexes, etc. Elles offrent une alternative ou un complément aux méthodes statistiques et mathématiques de modélisation de l'espace et du temps

18ème Atelier "Raisonnement à Partir de Cas" RàPC 2010

National audienceLe raisonnement à partir de cas (RàPC) est un paradigme de résolution de problèmes s'appuyant sur la réutilisation d'expériences passées pour résoudre de nouveaux problèmes. Les applications du RàPC sont nombreuses et la recherche est particuli'erement active en France et dans le monde. Les rencontres annuelles de la communauté fran¸caise ont été organisées depuis 1992 par le groupe français de recherche en RàPC, sous la forme d'ateliers d'un à deux jours, permettant de présenter et de discuter les travaux, théoriques ou appliqués, à différents stades d'avancement. Cette année 2010, le 18ème atelier RàPC est organisé à Strasbourg, en amont des assises du GDR I3 (" Information, Interaction, Intelligence "). À cette occasion, l'atelier RàPC partage une demi-journée avec les rencontres du thème IAF " Intelligence Artificielle Fondamentale " du GDR I3. Le programme complet est ainsi constitué de neuf présentations, huit soumises à l'atelier RàPC et une soumise aux journées IAF. Ces présentations sont réparties en quatre sessions : une première session porte sur des applications du RàPC à l'espace et aux déplacements ; une deuxième session (en deux temps) regroupe différents travaux sur l'adaptation ; les deux autres sessions sont consacrées pour l'une à la réutilisation d'expériences et à la remémoration, et pour l'autre à la comparaison du RàPC à d'autres méthodes appuyées sur l'expérience

Case Adaptation with Qualitative Algebras

This paper proposes an approach for the adaptation of spatial or temporal cases in a case-based reasoning system. Qualitative algebras are used as spatial and temporal knowledge representation languages. The intuition behind this adaptation approach is to apply a substitution and then repair potential inconsistencies, thanks to belief revision on qualitative algebras. A temporal example from the cooking domain is given. (The paper on which this extended abstract is based was the recipient of the best paper award of the 2012 International Conference on Case-Based Reasoning.

Automatic case acquisition from texts for process-oriented case-based reasoning

This paper introduces a method for the automatic acquisition of a rich case representation from free text for process-oriented case-based reasoning. Case engineering is among the most complicated and costly tasks in implementing a case-based reasoning system. This is especially so for process-oriented case-based reasoning, where more expressive case representations are generally used and, in our opinion, actually required for satisfactory case adaptation. In this context, the ability to acquire cases automatically from procedural texts is a major step forward in order to reason on processes. We therefore detail a methodology that makes case acquisition from processes described as free text possible, with special attention given to assembly instruction texts. This methodology extends the techniques we used to extract actions from cooking recipes. We argue that techniques taken from natural language processing are required for this task, and that they give satisfactory results. An evaluation based on our implemented prototype extracting workflows from recipe texts is provided.Comment: Sous presse, publication pr\'evue en 201

Belief revision in the propositional closure of a qualitative algebra

Belief revision is an operation that aims at modifying old be-liefs so that they become consistent with new ones. The issue of belief revision has been studied in various formalisms, in particular, in qualitative algebras (QAs) in which the result is a disjunction of belief bases that is not necessarily repre-sentable in a QA. This motivates the study of belief revision in formalisms extending QAs, namely, their propositional clo-sures: in such a closure, the result of belief revision belongs to the formalism. Moreover, this makes it possible to define a contraction operator thanks to the Harper identity. Belief revision in the propositional closure of QAs is studied, an al-gorithm for a family of revision operators is designed, and an open-source implementation is made freely available on the web

Hierarchies of Weighted Closed Partially-Ordered Patterns for Enhancing Sequential Data Analysis

International audienceDiscovering sequential patterns in sequence databases is an important data mining task. Recently, hierarchies of closed partially-ordered patterns (cpo-patterns), built directly using Relational Concept Analysis (RCA), have been proposed to simplify the interpretation step by highlighting how cpo-patterns relate to each other. However, there are practical cases (e.g. choosing interesting navigation paths in the obtained hierarchies) when these hierarchies are still insufficient for the expert. To address these cases, we propose to extract hierarchies of more informative cpo-patterns, namely weighted cpo-patterns (wcpo-patterns), by extending the RCA-based approach. These wcpo-patterns capture and explicitly show not only the order on itemsets but also their different influence on the analysed sequences. We illustrate how the proposed wcpo-patterns can enhance sequential data analysis on a toy example

Semi-automatic annotation process for procedural texts: An application on cooking recipes

Taaable is a case-based reasoning system that adapts cooking recipes to user constraints. Within it, the preparation part of recipes is formalised as a graph. This graph is a semantic representation of the sequence of instructions composing the cooking process and is used to compute the procedure adaptation, conjointly with the textual adaptation. It is composed of cooking actions and ingredients, among others, represented as vertices, and semantic relations between those, shown as arcs, and is built automatically thanks to natural language processing. The results of the automatic annotation process is often a disconnected graph, representing an incomplete annotation, or may contain errors. Therefore, a validating and correcting step is required. In this paper, we present an existing graphic tool named \kcatos, conceived for representing and editing decision trees, and show how it has been adapted and integrated in WikiTaaable, the semantic wiki in which the knowledge used by Taaable is stored. This interface provides the wiki users with a way to correct the case representation of the cooking process, improving at the same time the quality of the knowledge about cooking procedures stored in WikiTaaable

RCA-Seq: an Original Approach for Enhancing the Analysis of Sequential Data Based on Hierarchies of Multilevel Closed Partially-Ordered Patterns

International audienceMethods for analysing sequential data generally produce a huge number of sequential patterns that have then to be evaluated and interpreted by domain experts. To diminish this number and thus the difficulty of the interpretation task, methods that directly extract a more compact representation of sequential patterns, namely closed partially-ordered patterns (CPO-patterns), were introduced. In spite of the fewer number of obtained CPO-patterns, their analysis is still a challenging task for experts since they are unorgan-ised and besides, do not provide a global view of the discovered regularities. To address these problems, we present and formalise an original approach within the framework of Relational Concept Analysis (RCA), referred to as RCA-Seq, that focuses on facilitating the interpretation task of experts. The hierarchical RCA result allows to directly obtain and organize the relationships between the extracted CPO-patterns. Moreover, a generalisation order on items is also revealed, and multilevel CPO-patterns are obtained. Therefore, a hierarchy of such CPO-patterns guides the interpretation task, helps experts in better understanding the extracted patterns, and minimises the chance of overlooking interesting CPO-patterns. RCA-Seq is compared with another approach that relies on pattern structures. In addition, we highlight the adaptability of RCA-Seq by integrating a user-defined tax-* onomy over the items, and by considering user-specified constraints on the order relations on itemsets