129 research outputs found
Comparing Transport Quality Perception among Different Travellers in European Cities through Co-Cluster Analysis
No full textThe quality of the transport system offered at city level constitutes an important and challenging goal for society, for local authorities, and transport operators. Therefore, appropriate evaluation of travellers’ satisfaction is required to support service performance monitoring, benchmarking, and market analysis. This aspect implies the collection of satisfaction levels for different passengers’ groups, as it could provide interesting suggestions for identifying priority areas of action. To this end, an original study aimed at understanding the main aspects affecting the common view of satisfaction among different kinds of travellers at European level is presented in this paper. A specific survey investigating how travellers perceive the quality of their journey is proposed to people living in cities characterised by different sizes. Data are then analysed through a multi-view co-clustering algorithm, an innovative machine learning technique that highlights clusters of respondents grouped according to various categories of features. Such results could be used by local authorities and transport providers to understand the specific actions to be operated to improve the quality of transport service offered in a market segmentation dimension
Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining
No full text
Recommending Multimedia Objects in Cultural Heritage Applications
No full textAbstract. Italy’s Cultural Heritage is the world’s most diverse and rich patrimony and attracts millions of visitors every year to monuments, ar-chaeological sites and museums. The valorization of cultural heritage represents nowadays one of the most important research challenges in the Italian scenario. In this paper, we present a general multimedia rec-ommender system able to uniformly manage heterogeneous multimedia data and to provide context-aware recommendation techniques support-ing intelligent multimedia services for the users. A specific application of our system within the cultural heritage domain is proposed by means of a real case study in the mobile environment related to an outdoor scenario, together with preliminary results on user’s satisfaction.
A methodology for biologically relevant pattern discovery from gene expression data
No full textAbstract. One of the most exciting scientific challenges in functional ge-nomics concerns the discovery of biologically relevant patterns from gene expression data. For instance, it is extremely useful to provide putative synexpression groups or transcription modules to molecular biologists. We propose a methodology that has been proved useful in real cases. It is described as a prototypical KDD scenario which starts from raw expression data selection until useful patterns are delivered. Our concep-tual contribution is (a) to emphasize how to take the most from recent progress in constraint-based mining of set patterns, and (b) to propose a generic approach for gene expression data enrichment. The methodology has been validated on real data sets.
A Bi-clustering Framework for Categorical Data
No full textAbstract. Bi-clustering is a promising conceptual clustering approach. Within categorical data, it provides a collection of (possibly overlap-ping) bi-clusters, i.e., linked clusters for both objects and attribute-value pairs. We propose a generic framework for bi-clustering which enables to compute a bi-partition from collections of local patterns which capture locally strong associations between objects and properties. To validate this framework, we have studied in details the instance CDK-Means. It is a K-Means-like clustering on collections of formal concepts, i.e., con-nected closed sets on both dimensions. It enables to build bi-partitions with a user control on overlapping between bi-clusters. We provide an experimental validation on many benchmark datasets and discuss the interestingness of the computed bi-partitions.
Constraint-based mining of fault-tolerant patterns from boolean data
No full textAbstract. Thanks to an important research effort the last few years, in-ductive queries on local patterns (e.g., set patterns) and complete solvers which can evaluate them on large data sets have been proved extremely useful. The more we use such queries on real-life data, e.g., biological data (and thus intrinsically dirty and noisy), the more we are convinced that inductive queries should return fault-tolerant patterns. In this work, we consider user-defined constraints for a declarative specification of fault-tolerance. We discuss the design of such constraints on bi-sets extracted from Boolean data sets. Our starting point is the fundamental limita-tion of formal concept discovery (i.e., closed set mining) from noisy data and we propose a constraint-based mining approach for relevant fault-tolerant bi-set mining. Formalizing three recent proposals, our frame-work enables a better understanding of the needed trade-off between extraction feasibility, completeness, relevancy, and ease of interpretation of these fault-tolerant patterns. An original empirical evaluation on both synthetic and real-life medical data is given. It enables a comparison of the various proposals and it motivates further directions of research.
Un Cadre générique pour la co-classification sous contraintes: application à l'analyse du transcriptome
No full textThe search for interesting groups in boolean data (sets of objects described by sets of properties) has motivated the design of methods for computing global patterns (e. G. . , partitions), and extracting local patterns s(e. G. , frequent itemsets, association rules, formal concepts. This thesis concerns co-clustering, i. E. , computing bi-partitions (coupled partitions on both dimensions). When using available co-clustering algorithms, the user can hardly exploit his/her domain knowledge since he/she has limited possibilities for setting just a few parameters. On the other hand, classical local pattern mining techniques usually provide huge collections of patterns that are hard to evaluate and interpret. We have designed a new co-clustering framework which computes a bi-partition by starting from collections of patterns that capture locally strong associations (e. G. , formal concepts, delta-bi-set that are a form of fault-tolerant patterns). The idea is that the available information about the local patterns can be exploited to build a relevant global pattern. It becomes possible to consider the declarative specification of constraints on the bi-partitions (e. G. , user-defined requirements about the shape of clusters) and to use such constraints at the local pattern mining step and then during the co-clustering phase. As such, our proposal is a contribution to the recent domain of constraint-based clustering. A dual approach consists in using local patterns to interpret bi-partitions. We propose a method for bi-cluster characterization by means of local patterns and their associated interestingness measures. The application of our methods to a gene expression data analysis scenario has illustrated the added-value of our proposal to give rise to plausible biological hypothesis.La recherche de groupements intéressants dans les données booléennes (ensembles d'objets décrits par un ensemble de propriétés) a motivé la conception de méthodes d'extractions de motifs globaux (partitions) et de motifs locaux (ensembles fréquents, règles d'association et concepts formels). Cette thèse concerne la co-classification c'est-à-dire le calcul de bi-partitions (couplage de partitions sur les deux dimensions). Les algorithmes de co-classification disponibles ne permettent aux analystes d'exploiter leur connaissance du domaine qu'à travers un nombre réduit de paramètres. D'autre part, les techniques d'extraction de motifs locaux produisent d'énormes collections qui sont difficilement exploitables et interprétables. Nous avons développé une nouvelle méthode de co-classification qui calcule des bi-partitions à partir de motifs capturant des associations localement fortes (e. G. , des concepts formels, une forme de motif tolérant aux exceptions appelé delta-bi-ensemble). Le principe consiste à exploiter l'information contenue dans la collection des motifs locaux en la propageant au niveau global pour faciliter l'optimisation de la fonction objectif. Il devient alors possible de propager un certain nombre de contraintes depuis l'extraction des motifs locaux jusqu'à la construction de la bi-partition (e. G. , pour imposer des formes particulières aux groupes calculés). Il s'agit donc d'une contribution au domaine très récent de la classification sous contraintes. Une approche duale consiste à utiliser des motifs locaux pour faciliter l'interprétation de bi-partitions déjà calculées. Pour ce faire, nous proposons une méthode de caractérisation des bi-clusters au moyen de motifs locaux auxquels sont associés des mesures d'intérêt. L'application de nos méthodes à l'analyse de données d'expression de gènes a montré la pertinence de nos propositions pour expliciter des hypothèses biologiques plausibles
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