Céline Robardet

Closed patterns meet n -ary relations

Set pattern discovery from binary relations has been extensively studied during the last decade. In particular, many complete and efficient algorithms for frequent closed set mining are now available. Generalizing such a task to n-ary relations (n ≥ 2) appears as a timely challenge. It may be important for many applications, for example, when adding the time dimension to the popular objects × features binary case. The generality of the task (no assumption being made on the relation arity or on the size of its attribute domains) makes it computationally challenging. We introduce an algorithm called Data-Peeler. From an n-ary relation, it extracts all closed n-sets satisfying given piecewise (anti) monotonic constraints. This new class of constraints generalizes both monotonic and antimonotonic constraints. Considering the special case of ternary relations, Data-Peeler outperforms the state-of-the-art algorithms CubeMiner and Trias by orders of magnitude. These good performances must be granted to a new clever enumeration strategy allowing to efficiently enforce the closeness property. The relevance of the extracted closed n-sets is assessed on real-life 3-and 4-ary relations. Beyond natural 3-or 4-ary relations, expanding a relation with an additional attribute can help in enforcing rather abstract constraints such as the robustness with respect to binarization. Furthermore, a collection of closed n-sets is shown to be an excellent starting point to compute a tiling of the dataset.

Characterizing the speed and paths of shared bicycle use in Lyon

Data gathered relating to the Lyon’s shared bicycling system, Velo’v, is used to analyze 11.6 millions bicycle trips in the city. The data show that bicycles now compete with the car in terms of speed in downtown Lyon. It also provides information on cycle flows that can be of use in the planning of dedicated bicycle lanes and other facilities.

Description and simulation of dynamic mobility networks

During the last decade, the study of large scale complex networks has attracted a substantial amount of attention and works from several domains: sociology, biology, computer science, epidemiology. Most of such complex networks are inherently dynamic, with new vertices and links appearing while some old ones disappear. Until recently, the dynamics of these networks was less studied and there is a strong need for dynamic network models in order to sustain protocol performance evaluations and fundamental analyzes in all the research domains listed above. We propose in this paper a novel framework for the study of dynamic mobility networks. We address the characterization of dynamics by proposing an in-depth description and analysis of two real-world data sets. We show in particular that links creation and deletion processes are independent of other graph properties and that such networks exhibit a large number of possible configurations, from sparse to dense. From those observations, we propose simple yet very accurate models that allow generate random mobility graphs with similar temporal behavior as the one observed in experimental data.

Mining a new fault-tolerant pattern type as an alternative to formal concept discovery

Formal concept analysis has been proved to be useful to support knowledge discovery from boolean matrices. In many applications, such 0/1 data have to be computed from experimental data and it is common to miss some one values. Therefore, we extend formal concepts towards fault-tolerance. We define the DR-bi-set pattern domain by allowing some zero values to be inside the pattern. Crucial properties of formal concepts are preserved (number of zero values bounded on objects and attributes, maximality and availability of functions which “connect” the set components). DR-bi-sets are defined by constraints which are actively used by our correct and complete algorithm. Experimentation on both synthetic and real data validates the added-value of the DR-bi-sets.

Advances in Intelligent Data Analysis VIII

Invited Papers.- Intelligent Data Analysis in the 21st Century.- Analyzing the Localization of Retail Stores with Complex Systems Tools.- Selected Contributions 1 (Long Talks).- Change (Detection) You Can Believe in: Finding Distributional Shifts in Data Streams.- Exploiting Data Missingness in Bayesian Network Modeling.- DEMScale: Large Scale MDS Accounting for a Ridge Operator and Demographic Variables.- How to Control Clustering Results? Flexible Clustering Aggregation.- Compensation of Translational Displacement in Time Series Clustering Using Cross Correlation.- Context-Based Distance Learning for Categorical Data Clustering.- Semi-supervised Text Classification Using RBF Networks.- Improving k-NN for Human Cancer Classification Using the Gene Expression Profiles.- Subgroup Discovery for Test Selection: A Novel Approach and Its Application to Breast Cancer Diagnosis.- Trajectory Voting and Classification Based on Spatiotemporal Similarity in Moving Object Databases.- Leveraging Call Center Logs for Customer Behavior Prediction.- Condensed Representation of Sequential Patterns According to Frequency-Based Measures.- ART-Based Neural Networks for Multi-label Classification.- Two-Way Grouping by One-Way Topic Models.- Selecting and Weighting Data for Building Consensus Gene Regulatory Networks.- Incremental Bayesian Network Learning for Scalable Feature Selection.- Feature Extraction and Selection from Vibration Measurements for Structural Health Monitoring.- Zero-Inflated Boosted Ensembles for Rare Event Counts.- Selected Contributions 2 (Short Talks).- Mining the Temporal Dimension of the Information Propagation.- Adaptive Learning from Evolving Data Streams.- An Application of Intelligent Data Analysis Techniques to a Large Software Engineering Dataset.- Which Distance for the Identification and the Differentiation of Cell-Cycle Expressed Genes?.- Ontology-Driven KDD Process Composition.- Mining Frequent Gradual Itemsets from Large Databases.- Selecting Computer Architectures by Means of Control-Flow-Graph Mining.- Visualization-Driven Structural and Statistical Analysis of Turbulent Flows.- Distributed Algorithm for Computing Formal Concepts Using Map-Reduce Framework.- Multi-Optimisation Consensus Clustering.- Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences.- Measure of Similarity and Compactness in Competitive Space.- Bayesian Solutions to the Label Switching Problem.- Efficient Vertical Mining of Frequent Closures and Generators.- Isotonic Classification Trees.

Constraint-Based Mining of Formal Concepts in Transactional Data

We are designing new data mining techniques on boolean contexts to identify a priori interesting concepts, i.e., closed sets of objects (or transactions) and associated closed sets of attributes (or items). We propose a new algorithm D-Miner for mining concepts under constraints. We provide an experimental comparison with previous algorithms and an application to an original microarray dataset for which D-Miner is the only one that can mine all the concepts.

Constraint-Based Pattern Mining in Dynamic Graphs

Dynamic graphs are used to represent relationships between entities that evolve over time. Meaningful patterns in such structured data must capture strong interactions and their evolution over time. In social networks, such patterns can be seen as dynamic community structures, i.e., sets of individuals who strongly and repeatedly interact. In this paper, we propose a constraint-based mining approach to uncover evolving patterns. We propose to mine dense and isolated subgraphs defined by two user-parameterized constraints. The temporal evolution of such patterns is captured by associating a temporal event type to each identified subgraph. We consider five basic temporal events: The formation, dissolution, growth, diminution and stability of subgraphs from one time stamp to the next. We propose an algorithm that finds such subgraphs in a time series of graphs processed incrementally. The extraction is feasible due to efficient patterns and data pruning strategies. We demonstrate the applicability of our method on several real-world dynamic graphs and extract meaningful evolving communities.

A bi-clustering framework for categorical data

Bi-clustering is a promising conceptual clustering approach. Within categorical data, it provides a collection of (possibly overlapping) 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., connected 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.

Mining Graph Topological Patterns: Finding Covariations among Vertex Descriptors

We propose to mine the graph topology of a large attributed graph by finding regularities among vertex descriptors. Such descriptors are of two types: 1) the vertex attributes that convey the information of the vertices themselves and 2) some topological properties used to describe the connectivity of the vertices. These descriptors are mostly of numerical or ordinal types and their similarity can be captured by quantifying their covariation. Mining topological patterns relies on frequent pattern mining and graph topology analysis to reveal the links that exist between the relation encoded by the graph and the vertex attributes. We propose three interestingness measures of topological patterns that differ by the pairs of vertices considered while evaluating up and down co-variations between vertex descriptors. An efficient algorithm that combines search and pruning strategies to look for the most relevant topological patterns is presented. Besides a classical empirical study, we report case studies on four real-life networks showing that our approach provides valuable knowledge.

Parameter-less co-clustering for star-structured heterogeneous data

The availability of data represented with multiple features coming from heterogeneous domains is getting more and more common in real world applications. Such data represent objects of a certain type, connected to other types of data, the features, so that the overall data schema forms a star structure of inter-relationships. Co-clustering these data involves the specification of many parameters, such as the number of clusters for the object dimension and for all the features domains. In this paper we present a novel co-clustering algorithm for heterogeneous star-structured data that is parameter-less. This means that it does not require either the number of row clusters or the number of column clusters for the given feature spaces. Our approach optimizes the Goodman–Kruskal’s τ, a measure for cross-association in contingency tables that evaluates the strength of the relationship between two categorical variables. We extend τ to evaluate co-clustering solutions and in particular we apply it in a higher dimensional setting. We propose the algorithm CoStar which optimizes τ by a local search approach. We assess the performance of CoStar on publicly available datasets from the textual and image domains using objective external criteria. The results show that our approach outperforms state-of-the-art methods for the co-clustering of heterogeneous data, while it remains computationally efficient.