Wyniki wyszukiwania - BazTech

1

GrDBSCAN: A granular density-based clustering algorithm

Suchy Dawid, Siminski Krzysztof

International Journal of Applied Mathematics and Computer Science

|

2023

|

Vol. 33, no. 2

297--312

EN

Density-based spatial clustering of applications with noise (DBSCAN) is a commonly known and used algorithm for data clustering. It applies a density-based approach and can produce clusters of any shape. However, it has a drawback-its worst-case computational complexity is O(n2) with regard to the number of data items n. The paper presents GrDBSCAN: a granular modification of DBSCAN with reduced complexity. The proposed GrDBSCAN first granulates data into fuzzy granules and then runs density-based clustering on the resulting granules. The complexity of GrDBSCAN is linear with regard to the input data size and higher only for the number of granules. That number is, however, a parameter of the GrDBSCAN algorithm and is (significantly) lower than that of input data items. This results in shorter clustering time than in the case of DBSCAN. The paper is accompanied by numerical experiments. The implementation of GrDBSCAN is freely available from a public repository.

2

GrNFS: A granular neuro-fuzzy system for regression in large volume data

Siminski Krzysztof

International Journal of Applied Mathematics and Computer Science

|

2021

|

Vol. 31, no. 3

445--459

EN

Neuro-fuzzy systems have proved their ability to elaborate intelligible nonlinear models for presented data. However, their bottleneck is the volume of data. They have to read all data in order to produce a model. We apply the granular approach and propose a granular neuro-fuzzy system for large volume data. In our method the data are read by parts and granulated. In the next stage the fuzzy model is produced not on data but on granules. In the paper we introduce a novel type of granules: a fuzzy rule. In our system granules are represented by both regular data items and fuzzy rules. Fuzzy rules are a kind of data summaries. The experiments show that the proposed granular neuro-fuzzy system can produce intelligible models even for large volume datasets. The system outperforms the sampling techniques for large volume datasets.

3

Granular representation of the information potentialof variables –application example

Kiersztyn Adam, Gandzel Agnieszka, Celiński Maciej, Koczan Leopold

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2021

|

T. 11, nr 3

40--44

EN

With the introduction to the science paradigm of Granular Computing, in particular, information granules, the way of thinking about data has changed gradually. Both specialists and scientists stopped focusing on the single data records themselves, but began to look at the analyzed data in a broader context, closer to the way people think. This kind of knowledge representation is expressed, in particular, in approaches based on linguistic modelling or fuzzy techniques such as fuzzy clustering. Therefore, especially important from the point of view of the methodology of data research, is an attempt to understand their potential as information granules. In this study, we will present special cases of using the innovative method of representing the information potential of variables with the use of information granules. In a series of numerical experiments based on both artificially generated data and ecological data on changes in bird arrival dates in the context of climate change, we demonstrate the effectiveness of the proposed approach using classic, not fuzzy measures building information granules.

PL

Wraz z wprowadzeniem do nauki paradygmatu obliczeń ziarnistych, w szczególności ziaren informacji, sposób myślenia o danych stopniowo się zmieniał. Zarówno specjaliści, jak i naukowcy przestali skupiać się na samych rekordach pojedynczych danych, ale zaczęli patrzeć na analizowane dane w szerszym kontekście, bliższym ludzkiemu myśleniu. Ten rodzaj reprezentacji wiedzy wyraża się w szczególności w podejściach opartych na modelowaniu językowym lub technikach rozmytych, takich jak klasteryzacja rozmyta. Dlatego szczególnie ważna z punktu widzenia metodologii badania danych jest próba zrozumienia ich potencjału jako ziaren informacji. W niniejszym opracowaniu przedstawimy szczególne przypadki wykorzystania innowacyjnej metody reprezentacji potencjału informacyjnego zmiennych za pomocą ziaren informacji. W serii eksperymentów numerycznych opartych zarówno na danych generowanych sztucznie, jak i danych ekologicznych dotyczących zmian dat przylotów ptaków w kontekście zmian klimatycznych, demonstrujemy skuteczność proponowanego podejścia przy użyciu klasycznych, a nie rozmytych miar budujących ziarna informacji.

4

Granular filter in medical image noise suppression and edge preservation

Wieclawek Wojciech, Pietka Ewa

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 1

1--16

EN

An alternative non-linear filtering technique for medical image denoising while preserving edge is introduced. Two different variants of the approach i.e. crisp and fuzzy are developed. The solution is demonstrated based on US breast images as well as CT studies and gave promising results in comparison with commonly known and popular filtering techniques (i.e. spatial averaging and median, bilateral filter, anisotropic diffusion). Many different measures were used to evaluate the method. There are pixel-to-pixel error measures, structural information factors and edge preservation measures. The benefits are noticeable in all three categories.

5

Information granule system induced by a perceptual system

Bryniarska Anna

Annals of Computer Science and Information Systems

|

2019

|

Vol. 18

19--27

EN

Knowledge represented in the semantic network, especially in the Semantic Web, can be expressed in attributive language AL. Expressions of this language are interpreted in different theories of information granules: set theory, probability theory, possible data sets in the evidence systems, shadowed sets, fuzzy sets or rough sets. In order to unify the interpretations of expressions for different theories, it is assumed that expressions of the AL language can be interpreted in a chosen relational system called a granule system. In this paper, it is proposed to use information granule database and it is also demonstrated that this database can be induced by the measurement system of the adequacy of information retrieval, called a perceptual system. It can simplify previous formal description of the information granule system significantly. This paper also shows some examples of inducing rough and fuzzy granule databases by some perceptual systems.

6

Najnowsze zastosowania technologii Granular Computing, propozycja taksonomii oraz przydatności w cyberbezpieczeństwie

Pawlicki M., Choraś M., Kozik R.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

|

2018

|

nr 8-9

597--600, CD

PL

Wszechobecne ryzyko ataków teleinformatycznych sprawia, że poprawa jakości algorytmów wykrywania staje się sprawą najwyższej wagi. Technologia Granular Computing (GrC) daje nadzieję na nowy sposób polepszenia klasyfikacji ruchu sieciowego, wykrywania włamań i zmniejszenia wymogów obliczeniowych analizy ruchu sieciowego w czasie rzeczywistym. Niniejszy artykuł przedstawia podstawy Granular Computing, propozycję taksonomii oraz dyskusję przydatności technologii do uogólniania danych. Następnie zaprezentowany jest przegląd najnowszych zastosowań Granular Computing.

EN

With the prevailing risk of cybersecurity breaches, improving the detection algorithms is of utmost importance. We look forward to Granular Computing as a novel, promising way to improve network traffic classification, intrusion detection and reduction in the computational cost of real time traffic analysis. In this paper, a quick primer on granular computing is offered, its properties of abstracting data are looked into. Consecutively, a survey of the most recent Granular Computing implementations, and our proposal for taxonomy is presented.

7

Research on SDG fault diagnosis of ocean shipping boiler system based on fuzzy granular computing under data fusion

Zhu Y., Geng L.

Polish Maritime Research

|

2018

|

S 2

92--97

EN

The research work in this paper belongs to the application of granular computing, graph theory and its application in fault detection and diagnosis. It is a cross cutting and frontier research field in computer science, information science and graph theory. The results of this paper are of great significance to the application of the fault detection and diagnosis of the ocean boilers system. This research combines granular computing theory and signed directed graph, and proposes a new method of fault diagnosis, and applies it to the fault diagnosis of ocean ship boiler system.

8

A Granular Computing Method for OWL Ontologies

Lisi F. A., Mencar C.

Fundamenta Informaticae

|

2018

|

Vol. 159, nr 1/2

147--174

EN

We propose a method to extract and integrate fuzzy information granules from a populated OWL ontology. The purpose of this approach is to represent imprecise knowledge within an OWL ontology, as motivated by the fact that the Semantic Web is full of imprecise and uncertain information coming from perceptual data, incomplete data, data with errors, etc. In particular, we focus on Fuzzy Set Theory as a means for representing and processing information granules corresponding to imprecise concepts usually expressed by linguistic terms. The method applies to numerical data properties. The values of a property are first clustered to form a collection of fuzzy sets. Then, for each fuzzy set, the relative σ-count is computed and compared with a number of predefined fuzzy quantifiers, which are therefore used to define new assertions that are added to the original ontology. In this way, the extended ontology provides both a punctual view and a granular view of individuals w.r.t. the selected property. We use a real-world ontology concerning hotels and populated with data of the Italian city of Pisa, to illustrate the method and to test its implementation. We show that it is possible to extract granular properties that can be described in natural language and smoothly integrated in the original ontology by means of annotated assertions.

9

Interactive Logical Structures

Dutta S., Jankowski A., Skowron A.

Fundamenta Informaticae

|

2017

|

Vol. 154, nr 1/4

95--108

EN

We present an extension of logical structures, called interactive logical structures, for reasoning about interactive computations performed by Intelligent Systems or Complex Adaptive Systems. Reasoning based on such structures is called adaptive judgment and it goes beyond deduction, induction, and abduction. An extension of logical structures, based on complex granules, couples the abstract world and the physical world of an agent’s environment, and transmits the features of interactions of physical objects realized in the physical world to the abstract world. This allows us to consider the problems of perception and action.

10

The approach of granular computing and rough sets for identifying situations

Burov Ye., Mykich K.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

|

2017

|

Vol. 6, No 2

45--50

EN

In the article are described problems related to creation and maintenance of situational awareness systems. The definitions of concepts of situation and its identification are presented. An approach based on situational knowledge representation with ontological models is selected for attaining situational awareness in complex intelligent enterprise systems, where objects can be in several situations in the same time and some situations are defined imprecisely. Granular computing approach is used for reduction of situational knowledge management complexity. In order to work with situation defined imprecisely, rough set approximations are proposed for situation definition. The usage of mechanisms inherent to ontological modeling for situation representation and reasoning about them are also discussed.

11

Fuzzy number division and the multi-granularity phenomenon

Piegat A., Pluciński M.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2017

|

Vol. 65, nr 4

497--511

EN

The paper presents difficulties connected with fuzzy and interval division. If operations such as fuzzy addition, subtraction and multiplication provide as a result one compact, multidimensional granule, then a result of the fuzzy division can consists of few separated granules. Such results are more difficult to use in next calculations. The paper shows that the number of solution granules can be higher than 2 and that in certain problems division does not occur explicitly. In certain problems, separation of particular solution granules can be considerable. The paper also shows how to realize the fuzzy division when its denominator contains zero. Most types of fuzzy arithmetics forbid such operation. However, the paper shows that it is possible. Multidimensional fuzzy RDM arithmetic and horizontal membership functions which facilitate detecting of solution granules are also described. The considered problems are visualized by examples.

12

Granule based multiple objects tracking in wireless sensor and actor networks

Bernaś M., Płaczek B.

Studia Informatica

|

2016

|

Vol. 37, nr 2

5--14

EN

In proposed method sensor nodes provide data that allow actors to track and catch moving targets. Interval granules are used to take into account uncertain target positions and velocities. The wireless sensor nodes report position of a target only if length or direction of optimal actor's path to the target changes significantly. The effectiveness of the introduced method was experimentally evaluated in a simulation environment.

PL

W zaproponowanej metodzie węzły czujników dostarczają dane, które umożliwiają śledzenie ruchomych celów przez węzły mobilne (aktorów). Zastosowano ziarna informacji w formie przedziałów, aby uwzględnić niepewność danych (pozycji oraz prędkości celu). Węzły sieci sensorowej wysyłają informację o położeniu celu do aktora tylko wtedy, gdy odległość lub kierunek aktora odbiega znacząco od optymalnego. Skuteczność metody potwierdzono eksperymentalnie w środowisku symulacyjnym.

13

Rough Sets and Interactive Granular Computing

Skowron A., Jankowski A.

Fundamenta Informaticae

|

2016

|

Vol. 147, nr 2/3

371--385

EN

In several papers we have discussed a computing model, called the Interactive Granular Computing (IGrC), for interactive computations on complex granules. In this paper, we compare two models of computing, namely the Turing model and the IGrC model.

14

ANN as justified granular computing mechanism for medical data classification

Bernas M.

Journal of Medical Informatics & Technologies

|

2015

|

Vol. 24

85--90

EN

The medical data and its classification have to be treated in particular way. The data should not be modified or altered, because this could lead to false decisions. Most state-of-the-art classifiers are using random factors to produce higher overall accuracy of diagnosis, however the stability of classification can vary significantly. Medical support systems should be trustworthy and reliable, therefore this paper proposes fusion of multiple classifiers based on artificial Neural Network (ANN). The structure selection of ANN is performed using granular paradigm, where granulation level is defined by ANN complexity. The classification results are merged using voting procedure. Accuracy of the proposed solution was compared with state-of-the-art classifiers using real medical data coming from two medical datasets. Finally, some remarks and further research directions have been discussed.

15

Computing with words with the use of inverse RDM models of membership functions

Piegat A., Pluciński M.

International Journal of Applied Mathematics and Computer Science

|

2015

|

Vol. 25, no. 3

675--688

EN

Computing with words is a way to artificial, human-like thinking. The paper shows some new possibilities of solving difficult problems of computing with words which are offered by relative-distance-measure RDM models of fuzzy membership functions. Such models are based on RDM interval arithmetic. The way of calculation with words was shown using a specific problem of flight delay formulated by Lotfi Zadeh. The problem seems easy at first sight, but according to the authors’ knowledge it has not been solved yet. Results produced with the achieved solution were tested. The investigations also showed that computing with words sometimes offers possibilities of achieving better problem solutions than with the human mind.

16

Description Languages for Relational Information Granules

Hońko P.

Fundamenta Informaticae

|

2015

|

Vol. 137, nr 3

323--340

EN

Information granulation is a powerful tool for data analysis and processing. However, not much attention has been devoted to application of this tool to data stored in a relational structure. This paper extends the notion of information granules to a relational case. Two information systems intended to store relational data are proposed. This study also extends a granule description language to express information granules derived from relational data. The proposed approach enables to analyze a given problem at different levels of granularity of relational data. This can find application in searching for patterns in data mining.

17

A Granular Computing Approach to Symbolic Value Partitioning

Wen L.-Y., Min F.

Fundamenta Informaticae

|

2015

|

Vol. 142, nr 1-4

337--371

EN

Symbolic value partitioning is a knowledge reduction technique in the field of data mining. In this paper, we propose a granular computing approach for the partitioning task that includes granule construction and granule selection algorithms. The granule construction algorithm takes advantage of local information associated with each attribute. A binary attribute value taxonomy tree is built to merge these attribute values in a bottom-up manner using information-loss heuristics. The use of a balancing technique enables us to control different nodes in the same level to have approximately the same size. The granule selection algorithm uses global information about all of the attributes in the decision system. Hence, nodes across the taxonomy forest of all attributes are selected and expanded using information-gain heuristics. We present a series of experimental results that demonstrate the effectiveness of the proposed approach in terms of reducing the data size and improving the resulting classification accuracy.

18

The application of horizontal membership functions to fuzzy arithmetic operations

Tomaszewska K.

Journal of Theoretical and Applied Computer Science

|

2014

|

Vol. 8, nr 2

3--10

EN

The fuzzy numbers arise in decision making, control theory, fuzzy systems and approximate reasoning problems. The operations on them are becoming more and more popular. The aim of this paper is to present the fuzzy arithmetic operations on fuzzy numbers in a new way, using the horizontal membership functions (HMFs). The horizontal membership functions enable to introduce uncertain, interval or fuzzy variable-values together with crisp values in arithmetic operations without using Zadeh's extension principle. Thus, a relatively easy aggregation of crisp and uncertain knowledge has become possible. The numerical example of this developed method is also provided.

19

Rough Inclusion Functions and Similarity Indices

Gomolińska A., Wolski M.

Fundamenta Informaticae

|

2014

|

Vol. 133, nr 2/3

149--163

EN

Rough inclusion functions are mappings considered in rough set theory with which one can measure the degree of inclusion of a set (information granule) in a set (information granule) in line with rough mereology. On the other hand, similarity indices are mappings used in cluster analysis with which one can compare clusterings, and clustering methods with respect to similarity. In this article we show that a large number of similarity indices, known from the literature, can be generated by three simple rough inclusion functions, the standard rough inclusion function included.

20

Preorder Information Based Attributes’ Weights Learning in Multi-attribute Decision Making

Wang B., Liang J., Qian Y.

Fundamenta Informaticae

|

2014

|

Vol. 132, nr 3

331--347

EN

Choquet integral, as an adequate aggregation operator, extends the weighted mean operator by considering interactions among attributes. Choquet integral has been widely used in many real multi-attribute decision making. Weights (fuzzy measures) of attribute sets directly affect the decision results in multi-attribute decision making. In this paper, we aim to propose an objective method based on granular computing for determining the weights of the attribute sets. To address this issue, we first analyze the implied preorder relations under four evaluation forms and construct the corresponding preorder granular structures. Then, we define fuzzy measure of an attribute set by the similarity degree between a special preorder pairs. Finally, we employ two numerical examples for illustrating the feasibility and effectiveness of the proposed method. It is deserved to point out that the weight of each attribute subset can be learned from a given data set by the proposed method, not but be given subjectively by the decision maker. This idea provides a new perspective for multi-attribute decision making.