Aiming to improve the efficiency of urban traffic management, previous studies have achieved considerable traffic prediction accuracy. For example, methods based on time series analysis perform well in short-term traffic prediction, and neural networks show strong capabilities in processing complex nonlinear relationships within traffic data. However, previous studies also have the following two limitations: 1) a large amount of complex traffic data will increase the complexity of the model during training and further reduce the accuracy of the training results; 2) the large-scale distribution of traffic data leads to incomplete model training and data security issues. To address these issues, we propose a Formal Concept-enhanced Federated Graph Convolutional Network (FC-FedGCN), which adopts formal concept analysis to fully mine graph data and improve the training accuracy of the GCNs model. Under federated learning, the GCNs model can be trained independently on different clients, and the local model is optimized by sharing model parameters. Coupled with the premise of protecting data privacy, the integrity of the data is guaranteed and the training accuracy of the GCNs model is improved. We compare our model with various baseline models based on the PEMS datasets, and the results demonstrate that FC-FedGCN has significant advantages in traffic prediction, outperforming the comparison methods in multiple indicators.

Attributed social networks are pervasive in real life and play a crucial role in shaping various aspects of society. These networks not only capture the connections between individuals but also encompass the associated attributes and characteristics. Analyzing and understanding these attributes provide insights into social behaviors, information diffusion patterns, and the formation of influential communities. Consequently, we propose a novel algorithm for detecting fairness-aware maximal cliques in the attributed social networks. We extract the concept lattice of attributed social networks and quantify these concepts using the concept stability and fairness measures defined in this article. By utilizing the proposed fairness-aware distance, we identify fairness-aware maximal cliques within attributed social networks. The effectiveness of the algorithm is then validated using five real-world network datasets. Experimental results fully demonstrate the effectiveness and scalability of our approach in identifying key structures, analyzing attribute networks, and promoting the development of responsible computational systems.

In this paper, we revisit pattern mining and study the distribution underlying a binary dataset thanks to the closure structure which is based on passkeys, i.e., minimum generators in equivalence classes robust to noise. We introduce △-closedness, a generalization of the closure operator, where △ measures how a closed set differs from its upper neighbors in the partial order induced by closure. A △-class of equivalence includes minimum and maximum elements and allows us to characterize the distribution underlying the data. Moreover, the set of △-classes of equivalence can be partitioned into the so-called △-closure structure. In particular, a △-class of equivalence with a high △ is supported by more observations and thus is more stable. In the experiments, we study the △-closure structure of several real-world datasets and show that this structure is very stable for large △ and does not substantially depend on the data sampling used for the analysis.

Abstract Pattern mining is well established in data mining research, especially for mining binary datasets. Surprisingly, there is much less work about numerical pattern mining and this research area remains under-explored. In this paper we proposeMint, an efficient MDL-based algorithm for mining numerical datasets. The MDL principle is a robust and reliable framework widely used in pattern mining, and as well in subgroup discovery. InMintwe reuse MDL for discovering useful patterns and returning a set of non-redundant overlapping patterns with well-defined boundaries and covering meaningful groups of objects.Mintis not alone in the category of numerical pattern miners based on MDL. In the experiments presented in the paper we show thatMintoutperforms competitors among which IPD,RealKrimp, andSlim.

Pattern Mining (PM) has a prominent place in Data Science and finds its application in a wide range of domains. To avoid the exponential explosion of patterns different methods have been proposed. They are based on assumptions on interestingness and usually return very different pattern sets. In this paper we propose to use a compression-based objective as a well-justified and robust interestingness measure. We define the description lengths for datasets and use the Minimum Description Length principle (MDL) to find patterns that ensure the best compression. Our experiments show that the application of MDL to numerical data provides a small and characteristic subsets of patterns describing data in a compact way.

We solve the argument mining problem by investigating discourse and communicative text structure. A new formal graph-based structure called communicative discourse tree (CDT) is defined. It consists of a discourse tree with additional labels on edges, which stand for verbs. These verbs represent communicative actions. Discourse trees are based on rhetoric relations, extracted from a text according to Rhetoric Structure Theory. The problem is tackled as a binary classification task, where the positive class corresponds to texts with arguments and the negative class corresponds to texts with no arguments. The feature engineering for the classification task is conducted, deciding on which syntactic and discourse features are associated with logical argumentation. Text classification framework based on syntactic, discourse and communicative discourse text structures with a number of learning approaches is implemented. Evaluation on a combined data-set is provided.

A scalable method for mining graph patterns stable under subsampling is proposed. The existing subsample stability and robustness measures are not antimonotonic according to definitions known so far. We study a broader notion of antimonotonicity for graph patterns, so that measures of subsample stability become antimonotonic. Then we propose gSOFIA for mining the most subsample-stable graph patterns. The experiments on numerous graph datasets show that gSOFIA is very efficient for discovering subsample-stable graph patterns.

Pattern mining is an important task in AI for eliciting hypotheses from the data. When it comes to spatial data, the geo-coordinates are often considered independently as two different attributes. Consequently, rectangular patterns are searched for. Such an arbitrary form is not able to capture interesting regions in general. We thus introduce convex polygons, a good trade-off for capturing high density areas in any pattern mining task. Our contribution is threefold: (i) We formally introduce such patterns in Formal Concept Analysis (FCA), (ii) we give all the basic bricks for mining polygons with exhaustive search and pattern sampling, and (iii) we design several algorithms that we compare experimentally.

Relationships between proto-fuzzy concepts, crisply generated fuzzy concepts, and pattern structures are considered. It is shown that proto-fuzzy concepts are closely related to crisply generated fuzzy concepts in the sense that the mappings involved

An approach for studying relations between properties of functions on sets is proposed. The approach is based on Attribute Exploration. 16 properties of functions are considered, among them monotonicity, idempotency, path independence, exchange prope

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This paper shows how to embed a similarity relation between complex descriptions in concept lattices. We formalize similarity by a tolerance relation: objects are grouped within a same concept when having similar descriptions, extending the ability of FCA to deal with complex data. We propose two different approaches.~A first classical manner defines a discretization procedure. A second way consists in representing data by pattern structures, from which a pattern concept lattice can be constructed directly. In this case, considering a tolerance relation can be mathematically defined by a projection in a meet-semi-lattice. This allows to use concept lattices for their knowledge representation and reasoning abilities without transforming data. We show finally that resulting lattices are useful for solving information fusion problems.

One of the main problems to be solved while assisting inter-human conflict resolution is how to reuse the previous experience with similar agents. A machine learning technique for handling scenarios of interaction between conflicting human agents is proposed. Scenarios are represented by directed graphs with labelled vertices (for communicative actions) and arcs (for temporal and causal relationships between these actions and their parameters). For illustrative purposes, classification of a scenario is computed by comparing partial matching of its graph with graphs of positive and negative examples. Nearest Neighbour learning is followed by the JSM-based learning which minimised the number of false negatives and takes advantage of a more accurate way of matching sequences of communicative actions. Developed scenario representation and comparative analysis techniques are applied to the classification of textual customer complaints. It is shown that analysing the structure of communicative actions without context information is frequently sufficient to estimate complaint validity. Therefore, being domain-independent, proposed machine learning technique is a good compliment to a wide range of customer relation management applications where formal treatment of inter-human interactions is required in a decision-support mode.

MOTIVATION: A model for learning potential causes of toxicity from positive and negative examples and predicting toxicity for the dataset used in the Predictive Toxicology Challenge (PTC) is presented. The learning model assumes that the causes of toxicity can be given as substructures common to positive examples that are not substructures of negative examples. This assumption results in the choice of a learning model, called the JSM-method, and a language for representing chemical compounds, called the Fragmentary Code of Substructure Superposition (FCSS). By means of the latter, chemical compounds are represented as sets of substructures which are 'biologically meaningful' from the expert point of view. RESULTS: The chosen learning model and representation language show comparatively good performance for the PTC dataset: for three sex/species groups the predictions were ROC optimal, for one group the prediction was nearly optimal. The predictions tend to be conservative (few predictions and almost no errors), which can be explained by the specific features of the learning model. AVAILABILITY: by request to [email protected]; [email protected], http://ki-www2.intellektik.informatik.tu-darmstadt.de/~jsm/QDA.

Recently concept lattices became widely used tools for intelligent data analysis. In this paper, several algorithms that generate the set of all formal concepts and diagram graphs of concept lattices are considered. Some modifications of wellknown algorithms are proposed. Algorithmic complexity of the algorithms is studied both theoretically (in the worst case) and experimentally. Conditions of preferable use of some algorithms are given in terms of density/sparseness of underlying formal contexts. Principles of comparing practical performance of algorithms are discussed.