Intuitionistic fuzzy sets extend traditional fuzzy sets by incorporating degrees of membership, non-membership, and indeterminacy, making them particularly useful in contexts where uncertainty and hesitancy are prevalent. Rough soft sets combine rough sets' approximation capabilities with soft sets' flexible, parameterized approach to managing uncertainty. This study introduces Intuitionistic Fuzzy Rough Soft (IFRS) sets, integrating these advantages to create a robust framework for handling uncertainty, vagueness, and ambiguity in complex decision-making environments. The paper meticulously defines operations, operators, and measures between IFRS sets, establishing their characteristic properties through rigorous mathematical demonstrations. An innovative algorithm is proposed to address multi-criteria decision-making problems within this framework. The algorithm's effectiveness is thoroughly evaluated through comparisons with state-of-the-art algorithms using reputable datasets in medical consultation, agricultural land evaluation, educational support, and sensitivity analysis experiments. The results demonstrate the proposed algorithm's superior performance and robustness in complex decision-making scenarios, highlighting its potential as a valuable practical tool.

Knowledge mining from single graph plays an important role in decision support systems on single graphs such as social networks, bioinformatics, etc. In recent years, the problem of Frequent Subgraph Mining (FSM) from a single graph have been developed and attracted several studies. However, the problem of mining association rules or links from frequent subgraphs has not had many contributions. In this article, we state the problem of direct mining association rules from frequent subgraphs. Existing approaches on this topic perform the task in two phases. First, they traverse the search space to directly discover parent-child relationships from the discovered frequent subgraphs, then association rules are generated. We propose a one-phase algorithm, named So-GPARs, to generate rules as soon as frequent supergraphs are constructed from already existing frequent subgraphs. Our experiments on three single graph datasets show that the one-phase algorithm is more efficient than the two-phase algorithm in terms runtime of the rules generating phase.

In this article we present a set of data handling techniques and improvements in training a neural conversational model on big data. We approach this problem as a granular computing problem in which we focus on mini-batches creation as a problem of granules creation and relations between them to optimize the performance of the model. We also provide an empirical summary of influence on training time and model performance of different mini-batch creation strategies for training conversational neural model with personality. Additionally we present our own uniform length batching approach to speed up the training. We claim that with a mindful data preparation and use of data during training we are able to reduce compute time and infrastructure costs, thus allowing to train models on bigger data at reasonable time. The presented model is a chat-bot with topic awareness for a mobile service provider, which was trained on a large dataset of historical utterance data, that contained diverse subtopics and products. By applying data optimization techniques for handling and preparing batches of data, we can reduce the time of training and save a lot of time and compute costs without loosing performance of the model.

In this paper we propose a novel rough-fuzzy hybridization technique to feature selection and feature ranking problem.The idea is to model the local preference relation between pair of features by intuitionistic fuzzy values and search for a feature ranking that is consistent with those constraints.We apply the techniques used in group decision making where constraints are presented in form of intuitionistic fuzzy preference relation.The proposed method has been illustrated by some simple examples and verified on a benchmark dataset.

Convolution filters in deep convolutional networks display rotation variant behavior.While learned invariant behavior can be partially achieved, this paper shows that current methods of utilizing rotation variant features can be improved by proposing a grid-based graph convolutional network.By performing spectral graph convolutions on features extracted from subareas of images, we are able to take advantage of the geometric nature of relational machine learning in graph neural networks to be able to overcome rotation variant features to perform object localization.We demonstrate that Grid-GCN heavily outperforms existing models on rotated images, and through a set of ablation studies, we show how the performance of Grid-GCN implies that there exist more performant methods to utilize fundamentally rotation variant features and we conclude that the inherit nature of spectral graph convolutions is able to learn invariant behavior.

At the end of 2019 a new coronavirus emerged, turning into a world pandemic. The new coronavirus is called COVID-19. Different countries handled the pandemic differently and our main focus in this article is on Poland. For better counteracting and managing the situation a model for predicting the dynamics of the pandemic is needed. In this article we present a model for simulating future infections taking into account various preventive measures and locations in Poland. We based the model on a two-dimensional cellular automata, with spatial dependencies between regions, different population and size of simulated regions.

This paper introduces a novel, interactive and exploratory, approach to information retrieval (search engines) based on clustering. Presented method allows users to change the clustering structure by applying a free-text clustering context query that is treated as a criterion for document-to-cluster allocation. Exploration mechanisms are delivered by redefining the interaction scenario in which the user can interact with data on the level of topic discovery or cluster labeling. In this paper, the presented idea is realized by a graph structure called the Query-Summarize Graph. This data structure is useful in the definition of the similarity measure between the snippets as well as in the snippet clustering algorithm. The experiments on real-world data are showing that the proposed solution has many interesting properties and can be an alternative approach to interactive information retrieval.

In this paper we summarize AAIA'14 Data Mining Competition: Key risk factors for Polish State Fire Service which was held between February 3, 2014 and May 5, 2014 at the Knowledge Pit platform http://challenge.mimuw.edu.pl/. We describe the scope and background of this competition and we explain in details the evaluation procedure. We also briefly overview the results of this analytical challenge, showing the way in which those results can be beneficial to one of our other projects which is related to the problem of improving firefighter safety at a fire scene. Finally, we reveal some technical details regarding the architecture and functionalities of the Knowledge Pit competition platform, which we are developing in order to facilitate solving of practical problems that require advanced data analytics.

In this paper we consider a problem of automatic labeling of textual data with concepts explicitly defined in an external knowledge base.We describe our tagging system and we also present a framework for adaptive learning of associations between terms or phrases from the texts and the concepts.Those associations are then utilized by our semantic interpreter, which is based on the Explicit Semantic Analysis (ESA) method, in order to label scientific articles indexed by our SONCA platform.Apart from the description of the learning algorithm, we show a few practical application examples of our system, in which it was used for tagging scientific articles with headings from the MeSH ontology, categories from ACM Computing Classification System and from OECD Fields of Science and Technology Classification.

In this paper we describe an evacuation modeling framework based on a graph representation of the scene which is derived from its geometric description.Typically such graphs (geometric networks) are constructed using Medial Axis Transform (MAT) or Straight Medial Axis Transform (S-MAT).In our work we use Voronoi tessellation of a set of points approximating the scene (a single floor plan) along with the dual graph -Delaunay triangulation.Using these two graphs we extract not only the information about paths in the building, but also information about path widths and areas assigned to vertices.Typically only path lengths from MAT or S-MAT based geometric networks are used in evacuation modeling.Our approach enables us to include flow analysis and e.g.locate bottlenecks.We discuss a typical density-based evacuation model coupled with a partial behavioral evacuation model within proposed framework.

This issue contains six papers presented during the Ninth IEEE RIVF International Conference onComputing and Communication Technologies (RIVF 2012), held in Ho Chi Minh City (Vietnam) in the period Feb. 27 -Mar.01, 2012.Since its inception in 2003, the RIVF conference -Research, Innovation, and Vision for the Future -has become a major international scientific event in the field of Computing, Communication and Information Technologies.RIVF 2012 received 141 papers from 26 countries, submitted to seven specialised tracks of the conference.Each submission was first evaluated by at least two reviewers, and over two third of them by three or four reviewers, followed by discussions with the track chairs.Finally, the program chairs had an overall review of the recommendations by the track chairs, and decided to accept 35 long papers and 24 short papers, resulting in the acceptance rates of 41.8%.The conference program also included a poster session for authors to present their on-going work.The authors of top ten papers that received the highest evaluation scores were invited to submit the extended versions of their contributions.After an additional review process (at least two reviewers for each paper), six papers were selected and included in this special issue.To help the reader to get a better insight into this special issue, we provide brief overviews for each of the papers of this issue.In the paper Quadratic Algorithms for Testing of Codes and ⋄-Codes, Nguyen Dinh Han, Ho Ngoc Vinh, Dang Quyet Thang and Phan Trung Huy present a modification of the Sardinas-Patterson's test that can deduce more effective testing algorithm for codes.As a consequence, for a given at input a regular language X defined by a tuple (ϕ, M, B), where ϕ : A * → M is a monoid morphism saturating X, M is a finite monoid, B ⊆ M , X = ϕ -1 (B), the authors established an algorithm that decides in time O(n 2 ) whether X is a code, where n = |M | can be chosen as the finite index of X.Also the quadratic algorithm for testing of ⋄-codes is also established.

In this article we propose a general framework incorporating semantic indexing and search of texts within scientific document repositories. In our approach, a semantic interpreter, which can be seen as a tool for automatic tagging of textual data, is

Concept learning in description logics (DLs) is similar to binary classification in traditional machine learning. The difference is that in DLs objects are described not only by attributes but also by binary relationships between objects. In this pap

This paper summarizes the some of the recent developments in the area of application of rough sets and granular computing in hierarchical learning. We present the general framework of rough set based hierarchical learning. In particular, we investigate several strategies of choosing the appropriate learning algorithms for first level concepts as well as the learning methods for the intermediate concepts. We also propose some techniques for embedding the domain knowledge into the granular, layered learning process in order to improve the quality of hierarchical classifiers. This idea, which has been envisioned and developed by professor Andrzej Skowron over the last 10 years, shows to be very efficient in many practical applications. Throughout the article, we illustrate the proposed methodology with three case studies in the area of pattern recognition. The studies demonstrate the viability of this approach for such problems as: sunspot classification, hand-written digit recognition, and car identification.

It is a big challenge for an information retrieval system (IRS) to interpret the queries made by users, particularly because the common form of query consists of very few terms. Tolerance rough sets models (TRSM), as an extension of rough sets theory

This very special, 2 7.0 – 1 volume of Fundamenta Informaticae is dedicated to Andrzej Skowron on the occasion of his 70 th birthday. The contributions are on an invitational basis, but they have been reviewed according to the usual standards of the journal. The editors want to thank all the contributors and reviewers for their great work. Without it, this volume would be much less special. It is very hard, if even possible, to describe Andrzej Skowron in a finite collection of words. He is such an unique personality and scientist. To get some understanding what he is like it may help to read the accounts included in this preface. These accounts are provided by persons who interact with Andrzej for years on both professional and personal grounds: Roman Świniarski with family, Janusz Kacprzyk, Damian Niwiński, and Stanisław Matwin. When we started to circulate the idea of this special volume among Andrzej's extended scientific family, we have met an enthusiastic response. So enthusiastic in fact, that we were initially a little bit overwhelmed. Everybody wanted to be on board. We managed to convince several groups of researchers to join forces and write one comprehensive, yet compact article instead of several. In this way, it was possible to fit the material in one, thirty-six-piece volume. The thirty-six articles that make this special volume of Fundamenta Informaticae span over a very wide range of topics. They reflect Andrzej Skowron's activities as a researcher and a scholar as well as his influence on a broad scientific community. In order to make this volume more approachable we have ordered the papers with respect to general areas their represent. To do that we have used a methodology that has quite bit to do with results of one of the research projects Andrzej was recently involved in. Namely, we have manually performed a semantic clustering of our contribution pool. As a result the papers have been organized into four disjoint clusters (thematic groups) that we briefly introduce below. First of the clusters gathers articles that correspond to some fundamental directions in recent and past research of Andrzej Skowron. The reader will find in this cluster papers representing such areas as: foundations of rough sets, logical aspects of both rough and related models of computation, foundational issues relating to logical aspects of non-classical computational systems, formal and computational aspects of inference systems, and nature-inspired computational systems. In this cluster we have contributions by: Mihir K. Chakraborty and Mohua Banerjee; Anna Gomolińska and Marcin Wolski; Ewa Orłowska and Ivo Düntsch; Yiyu Yao; Lech Polkowski and Maria Semeniuk-Polkowska; Ludwik Czaja; Grzegorz Rozenberg, Gheorghe Paun, and Mario J. Perez-Jimenez; Alberto Pettorossi, Fabio Fioravanti, Maurizio Proietti, and Valerio Senni; Andrzej Szałas and Patrick Doherty. The second cluster contains papers that describe research results in topics associated with discovering, representing and making use of knowledge learned form data. In particular, several of approaches described in these papers make use of reducts and decision rules. The contributions made by Andrzej Skowron to methods and algorithms for representation, reduction, and simplification of information retrieved from data are instrumental here. There are also papers that deal with approximations and approximation spaces, an area pioneered by Andrzej. Members of this cluster are papers by: Mikhail Moshkov, Talha Amin, Igor Chikalov, and Beata Zielosko; Roman Słowiński, Salvatore Greco, and Izabela Szczęch; Jerzy Grzymała-Busse and Patrick G. Clark; Wojciech Ziarko and Xugunag Chen; Shusaku Tsumoto and Shoji Hirano; Zbigniew Raś and Hakim Touati; Hui Wang and Ivo Düntsch; Zbigniew Suraj and Krzysztof Pancerz; Jan Komorowski, Marcin Kruczyk, Nicholas Baltzer, Jakub Mieczkowski, Michał Dramiński, and Jacek Koronacki. The third group of contributions relates to another large area of research on which Andrzej Skowron left his mark. The papers represent studies on fundamentals and applications of granular approach to knowledge-based systems as well as investigations into underlying notions of closeness, similarity, and nearness. They also address challenges associated with construction and usage of granular systems – in particular multi-layered, hierarchical ones – in knowledge discovery and decision support. Papers by the following authors make this group: Sankar K. Pal, Jayanta Kumar Pal, and Shubhra Sankar Ray; Marzena Kryszkiewicz; Bożena Kostek and Andrzej Kaczmarek; Alicja Wakulicz-Deja, Agnieszka Nowak-Brzezińska, and Małgorzata Przybyła-Kasperek; James Peters and Sheela Ramanna; Hung Son Nguyen, Sinh Hoa Nguyen, Tuan Trung Nguyen, and Marcin Szczuka; Guoyin Wang, Yuchao Liu, Deyi Li, and Wen He; Witold Pedrycz; Tsau Young Lin, Yong Liu, and Wenliang Huang. The fourth and final group contains nine papers that represent a little wider range of topics. Among them are papers that deal with data processing in general, including research related to database technology as well as search techniques. There are papers in this cluster that deal with data and knowledge representation and navigation. There are also described various aspects of data mining including those that make use of multiagent approach as well as methods based on processing of visual information. In this cluster the reader will find contributions by: Jarosław Stepaniuk, Maciej Kopczyński, and Tomasz Grzes; Dominik Ślęzak, Piotr Synak, Arkadiusz Wojna, and Jakub Wróblewski; Henryk Rybiński and Jacek Lewandowski; Jiming Liu, Hao Lan Zhang, and Yanchun Zhang; Jan G. Bazan, Andrzej Jankowski, and Sylwia Buregwa-Czuma; Wojciech Froelich, Rafał Deja, and Grażyna Deja; Piotr Wasilewski and Adam Krasuski; Ning Zhong, Linchan Qin, Shengfu Lu, and Mi Li; Andrzej Czyżewski and Karol Lisowski. The editors of this special volume would like to wish a Happy Birthday to Andrzej and hope that he will like this little gift. Dominik Ślęzak Hung Son Nguyen Marcin Szczuka October 2013

This special issue of Fundamenta Informaticae contains a selection of papers initially presented at the 6th International Conference on Rough Sets and Knowledge Technology (RSKT'11) held during October 8-11, 2011 in Banff, Canada.RSKT is an international scientific conference series that has been held every year since 2006.The conferences serve as a major forum that brings researchers and industry practitioners together to discuss and deliberate on fundamental issues of knowledge processing and management and knowledge-intensive practical solutions in the current knowledge age.Experts from around the world meet to present state-of-the-art scientific results, to nurture academic and industrial interaction, and to promote collaborative research in rough sets and knowledge technology.We initially had twelve papers invited.After rigorous review, eight papers were selected to be included in this issue.They are substantially extended versions of respective conference papers.Each paper was review by three domain experts and went through at least two rounds of revisions.

The Tolerance Rough Sets Model (TRSM) is a tool to model document in text mining. Generation of a document representation based on TRSM basically depends on tolerance classes of terms which are created by setting up a tolerance value. Despite the fact that tolerance value is critical in TRSM, the manual process of setting this value is an exhaustive task. We conducted a study on our own corpus and were supported by two human experts when constructing the training data. We came up with a novel algorithm to generate tolerance value automatically from a set of training data. The heart of our algorithm is measuring the distance between document representation calculated using TFIDF weighting scheme and document representation yielded by TRSM both in reduced dimensional space generated by Singular Value Decomposition (SVD). In spite of the result, we recognize that further study is significant, i.e. to set up some properties (the size of training data and rank of SVD) as well as evaluating the algorithm in real data and scenario.

This paper presents a research on the construction of a new unsupervised model for learning a semantic similarity measure from text corpora. Two main components of the model are a semantic interpreter of texts and a similarity function whose properti

contained 86 papers selected from 229 submissions.After the additional peer reviewing process, we accepted seven revised and extended articles for publication in this issue.We believe that they reflect a variety of current aspects of knowledge technology research and applications, with rough sets treated as one of useful methodologies.The first paper, by Taichi Haruna and Yukio-Pegio Gunji, is titled "Double Approximation and Complete Lattices".The authors discuss rough set based double approximation spaces from a lattice theoretic point of view.They introduce a generalization of complete atomic Boolean algebras called a complete prime lattice.They also prove an adjunction between the category of semiprime double approximation systems and the dual of the category of complete prime lattices.The results enrich rough set theory, especially, for the categorical formulation of pointless topology.The second paper, by Tong-Jun Li and Wei-Zhi Wu, is titled "Attribute Reduction in Formal Contexts: A Covering Rough Set Approach".The authors introduce notions of reducible attributes and irreducible attributes in a formal context and give some judgment theorems which can determine all attribute reducts in the formal context.They partition all attributes of a formal context into three types: absolutely necessary attributes, relatively necessary attributes, and unnecessary attributes.They further employ properties of irreducible classes of the formal context to characterize each type of attributes.The proposed method provides a new hybrid knowledge representation model that combines rough sets and concept lattices.The third paper, by Yan-Qing Yao, Ju-Sheng Mi, Zhoujun Li, and Bin Xie, is titled "The Construction of Fuzzy Concept Lattices Based on (θ, σ)-Fuzzy Rough Approximation Operators".The authors develop approaches to construct fuzzy concept lattices based on generalized fuzzy rough approximation operators.They introduce and examine some pairs of fuzzy rough upper and lower approximation operators over fuzzy formal contexts, via a residual implicator θ satisfying condition θ(a, b) = θ(1 -b, 1 -a).Using such approximation operators, they obtain three theoretical approaches to constructing fuzzy concept lattices.This work leads to a potential application to data analysis, based on rough set theory and formal concept analysis.The fourth paper, by Zhengjiang Wu, Tianrui Li, Keyun Qin, and Da Ruan, is titled "Approximation Operators, Binary Relation and Basis Algebra in L-Fuzzy Rough Sets".The authors present constructive and axiomatic approaches for the study of generalized fuzzy rough sets based on residuated lattices

Due to the enormous size of the Web and low precision of user queries, finding the right information from the Web can be difficult if not impossible. One approach that tries to solve this problem is using clustering techniques for grouping similar document together in order to facilitate presentation of results in more compact form and enable thematic browsing of the results set. The main problem of many Web search result (snippet) clustering algorithm is based on the poor vector representation of snippets. In this paper, we present a method of snippet representation enrichment using tolerance rough set model. We applied the proposed method to construct a rough set based search result clustering algorithm and compared it with other recent methods.

We discuss information granule calculi as a basis of granular computing. They are defined by constructs like information granules, basic relations of inclusion and closeness between information granules as well as operations on them. The exact interpretation between granule languages of different information sources (agents) often does not exist. Hence (rough) inclusion and closeness of granules are considered instead of their equality. Examples of all the basic constructs of information granule calculi are presented. The construction of more complex information granules is described by expressions called terms. We discuss the synthesis problem of robust terms, i.e., descriptions of information granules, satisfying a given specification in a satisfactory degree. We also present a method for synthesis of information granules represented by robust terms (approximate schemes of reasoning) by means of decomposition of specifications for such granules. The discussed problems of granular computing are of special importance for many applications, in particular related to spatial reasoning as well as to knowledge discovery and data mining.

ASSOCIATION RULE (see [1]) extraction methods have been developed as the main methods for mining of real life data, in particular in Basket Data Analysis. In this paper we present a novel approach to generation of association rules, based on Rough Set and Boolean reasoning methods. Some results presented in this paper has been mentioned in [13, 17]. We will explain them precisely (with full proofs of theorems) in this paper. We show the relationship between the problems of association rule extraction for transaction data and relative reducts (or α-reducts generation) for a decision table. Moreover, the present approach can be used to extract association rules in general form. The experimental results show that the presented methods are quite efficient. Large number of association rules with given support and confidence can be extracted in a short time.

We present an optimal hyperplane searching method for decision tables using Genetic Algorithms. This method can be used to construct a decision tree for a given decision table. We also present some properties of the set of hyperplanes determined by our methods and evaluate an upper bound on the depth of the constructed decision tree.

Searching for patterns is one of the main goals in data mining. Patterns have important applications in many KDD domains like rule extraction or classification. In this paper we present some methods of rule extraction by generalizing the existing approaches for the pattern problem. These methods, called partition of attribute values or grouping of attribute values, can be applied to decision tables with symbolic value attributes. If data tables contain symbolic and numeric attributes, some of the proposed methods can be used jointly with discretization methods. Moreover, these methods are applicable for incomplete data. The optimization problems for grouping of attribute values are either NP-complete or NP-hard. Hence we propose some heuristics returning approximate solutions for such problems.