Section: New Results

The Mining of Complex Data

Participants : Mehwish Alam, Aleksey Buzmakov, Melisachew Chekol, Victor Codocedo, Adrien Coulet, Elias Egho, Nicolas Jay, Florence Le Ber, Ioanna Lykourentzou, Luis-Felipe Melo, Amedeo Napoli, Chedy Raïssi, Mohsen Sayed, My Thao Tang, Mohsen Sayed, Yannick Toussaint.

Keywords:

formal concept analysis, relational concept analysis, pattern structures, frequent itemset, association rule, graph mining, sequence mining, skyline

Formal Concept Analysis, together with itemset search and association rule extraction, are suitable symbolic methods for KDDK, that may be used for real-sized applications. Global improvements are carried on the scope of applicability, the ease of use, the efficiency of the methods, and on the ability to fit evolving situations. Accordingly, the team is extending these symbolic data mining methods for working on biological or chemical data or textual documents, involving objects with multi-valued attributes (e.g. domains or intervals), n-ary relations, sequences, trees and graphs.

FCA and variations: RCA and Pattern Structures

There are a few extensions of FCA for handling contexts involving complex data formats, e.g. graphs or relational data. Among them, Relational Concept Analysis (RCA) is a process for analyzing objects described both by binary and relational attributes [10] . The RCA process takes as input a collection of contexts and of inter-context relations, and yields a set of lattices, one per context, whose concepts are linked by relations. RCA has an important role in KDDK, especially in text mining [86] , [85] .

Another extension of FCA is based on Pattern Structures (PS) [92] , which allows to build a concept lattice from complex data, e.g. nominal, numerical, and interval data. In [100] , pattern structures are used for building a concept lattice from interval data. Since then, we worked on a some experiments involving pattern structures, namely sequence mining [41] , information retrieval [48] and functional dependencies [38] . one of the next step is the adaptation of pattern structures to graph mining. Moreover, the notion of similarity between objects is also closely related to pattern structures [99] : two objects are similar as soon as they share the same attributes (binary case) or attributes with similar values or the same description (at least in part). Combination of similarity and pattern structures is also under study, in particular for solving information retrieval and annotation problems.

Finally, there is also an on-going work relating FCA and semantic web. This work focuses on the classification within a concept lattice of the answers returned by SPARQL queries [37] , [47] , [46] , [44] . The concept lattice is then used as an index for navigating and ranking the answers w.r.t. their content and interest for a given objective.

Advances in mining complex data: sequences and healthcare trajectories

Sequence data is widely used in many applications. Consequently, mining sequential patterns and other types of knowledge from sequence data has become an important data mining task. The main emphasis has been on developing efficient mining algorithms and effective pattern representation. The most frequent sequences generally provide a trivial information. When analyzing the set of frequent sequences with a low minimum support, the user is overwhelmed by millions of patterns. In our recent work, the general idea is to extract patterns whose characteristic on a given measure such as the support strongly deviates from its expected value under a null model. The frequency of a pattern is considered as a random variable, whose distribution under the null model has to be calculated or approximated. Then, the significance of the pattern is assessed through a statistical test that compares the expected frequency under the null model to the observed frequency. One of the key-points of this family of approaches is to choose an appropriate null model. It will ideally be a trade-off between adjustment to the data and simplicity: the model should capture some characteristics of the data, to integrate prior knowledge, without overfitting, to allow for relevant patterns discovery. We introduced a rigorous and efficient approach to mine statistically significant, unexpected patterns in sequences of itemsets. Experiments on sequences of replays of a video game demonstrated the scalability and the efficiency of the method to discover unexpected game strategies. This work was successfully published as an international conference paper [8] .

Other work on sequences is in concern with patient trajectories, i.e. the “path” of a patient during its illness. With the increasing burden of chronic illnesses, administrative health care databases hold valuable information that could be used to monitor and assess the processes shaping the trajectory of care of chronic patients. In this context, temporal data mining methods are promising tools, though lacking flexibility in addressing the complex nature of medical events. In a set of recent works with Elias Egho, a PhD candidate, we present new algorithms to extract patient trajectory patterns with different levels of granularity by relying on external taxonomies [52] . Our algorithms rely on the general FCA framework to formalize the general notion of multidimensional healthcare trajectories. We also continued working on the complex notion of sequences or trajectory similarity measures. We show the interest of our approaches with the analysis of trajectories of care for colorectal cancer using data from the French healthcare information system (see also [41] ).

KDDK in Text Mining

Ontologies help software and human agents to communicate by providing shared and common domain knowledge, and by supporting various tasks, e.g. problem-solving and information retrieval. In practice, building an ontology depends on a number of “ontological resources” having different types: thesaurus, dictionaries, texts, databases, and ontologies themselves. We are currently working on the design of a methodology and the implementation of a system for ontology engineering from heterogeneous ontological resources [58] . This methodology is based on both FCA and RCA, and was previously successfully applied in contexts such as astronomy and biology. In the framework of the ANR Hybride project (see  8.2.1.2 ), an engineer is implementing a robust system based on these previous research results, for preparing the way to new research directions involving trees and graphs.