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1

On knowledge discovery and representations of molecular structures using topological indices

Alsaadi Fawaz E., Bokhary Syed Ahtsham Ul Haq, Shah Aqsa, Ali Usman, Cao Jinde, Alassafi Madini Obad, Rehman Masood Ur, Rahman Jamshaid Ul

Journal of Artificial Intelligence and Soft Computing Research

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2021

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Vol. 11, No. 1

21--32

EN

The main purpose of a topological index is to encode a chemical structure by a number. A topological index is a graph invariant, which decribes the topology of the graph and remains constant under a graph automorphism. Topological indices play a wide role in the study of QSAR (quantitative structure-activity relationship) and QSPR (quantitative structure-property relationship). Topological indices are implemented to judge the bioactivity of chemical compounds. In this article, we compute the ABC (atom-bond connectivity); ABC4 (fourth version of ABC), GA(geometric arithmetic) and GA5(fifth version of GA) indices of some networks sheet. These networks include: octonano window sheet; equilateral triangular tetra sheet; rectangular sheet; and rectangular tetra sheet networks.

2

Rule based networks : an efficient and interpretable representation of computational models

Liu H., Gegov A., Cocea M.

Journal of Artificial Intelligence and Soft Computing Research

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2017

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Vol. 7, No. 2

111--123

EN

Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. Rule learning methods, a special type of machine learning methods, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modelling. A rule based system may be represented through different structures. The techniques for representing rules are known as rule representation, which is significant for knowledge discovery in relation to the interpretability of the model, as well as for predictive modelling with regard to efficiency in predicting unseen instances. This paper justifies the significance of rule representation and presents several existing representation techniques. Two types of novel networked topologies for rule representation are developed against existing techniques. This paper also includes complexity analysis of the networked topologies in order to show their advantages comparing with the existing techniques in terms of model interpretability and computational efficiency.

3

Zastosowanie metod eksploracji danych do wspomagania przygotowania procesu dydaktycznego

Sidor K.

Zeszyty Naukowe Politechniki Rzeszowskiej. Elektrotechnika

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2016

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z. 35 [294], nr 1

27--38

PL

Eksploracja danych dostarcza cennej wiedzy ukrytej w dużych zbiorach danych. Pozwala na odkrywanie zależności niewidocznych gołym okiem. Swoje zastosowanie może znaleźć także w edukacji podczas przygotowywania oferty dydaktycznej. Artykuł przedstawia zastosowanie algorytmów eksploracji danych w przygotowaniu procesu edukacyjnego. W rozważanym zakresie eksploracja danych służy do przekształcania surowych danych w wiedzę, która pozwala na poznanie preferencji studentów. Skupiono się na odkrywaniu grup studentów oraz tworzeniu ich modeli określających style uczenia się. W trakcie budowania grup zastosowano klasyfikację bez nadzoru m.in. metody k-średnich oraz EM. Grupy tworzone były z uwzględnieniem preferencji studentów dotyczących nauki. Pozwoliło to na uzyskanie grup zawierających studentów o podobnych stylach uczenia się. Do zweryfikowania poprawności klasyfikacji wykorzystane zostały indeksy walidacyjne, które pozwoliły na wybranie najbardziej efektywnego podziału studentów. Badania przeprowadzono na danych zebranych wśród studentów Politechniki Rzeszowskiej na podstawie ankiety zawierającej kwestionariusz ILS. Uzyskane podczas badań wyniki pozwoliły na określenie ile różnorodnych materiałów dydaktycznych należy przygotować, aby były dopasowane do preferencji studentów różnych grup. Poznanie stylów uczenia się studentów pozwala nauczycielowi na lepsze zrozumienie upodobań studentów, a samym uczniom na dopasowanie materiałów do własnego stylu uczenia, dzięki czemu łatwiej i szybciej przyswajają wiedzę.

EN

Data mining provides valuable knowledge hidden in large data sets. It allows to explore depending invisible to the naked eye. It has been used in education while preparation educational offer. The article shows the application of data mining algorithms in the preparation of the educational process. In the considered range, data mining is used to transform raw data into knowledge, which allows to know the students' preferences. It has been focused on discovering groups of students and the development of models for the assessment of their learning styles. It has been applied unsupervised classification during process build groups. Groups have been created taking into account the preferences of students in science. It has been allowed get the groups consisting of students with similar learning styles. To verify the accuracy of the classification has been used indexes validation that allowed you to select the most efficient distribution of students. The study was conducted on data collected among students of Rzeszow University of Technology based on a survey questionnaire containing the ILS. Obtained during the studies results allowed to determine what materials teaching should be prepared to be tailored to the preferences of different groups of students. Understanding the learning styles of students allows teachers to better understand the preferences of students and the students to tailor materials to their own learning style, making it easier and faster to acquire knowledge.

4

Methodologies of knowledge discovery from data and data mining methods in mechanical engineering

Rogalewicz M., Sika R.

Management and Production Engineering Review

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2016

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Vol. 7, No. 4

97--108

EN

The paper contains a review of methodologies of a process of knowledge discovery from data and methods of data exploration (Data Mining), which are the most frequently used in mechanical engineering. The methodologies contain various scenarios of data exploring, while DM methods are used in their scope. The paper shows premises for use of DM methods in industry, as well as their advantages and disadvantages. Development of methodologies of knowledge discovery from data is also presented, along with a classification of the most widespread Data Mining methods, divided by type of realized tasks. The paper is summarized by presentation of selected Data Mining applications in mechanical engineering.

5

About New Version of RSDS System

Suraj Z., Grochowalski P.

Fundamenta Informaticae

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2014

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Vol. 135, nr 4

503--519

EN

The aim of this paper is to present a new version of a bibliographic database system - Rough Set Database System (RSDS). The RSDS system, among others, includes bibliographic descriptions of publications on rough set theory and its applications. This system is also an experimental environment for research related to the processing of bibliographic data using the domain knowledge and the related information retrieval.

6

Discovering knowledge with the rough set approach

Mazurek J.

Polish Journal of Management Studies

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2013

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Vol. 7

245--254

EN

The rough set theory, which originated in the early 1980s, provides an alternative approach to the fuzzy set theory, when dealing with uncertainty, vagueness or inconsistence often encountered in real-world situations. The fundamental premise of the rough set theory is that every object of the universe is associated with some information, which is frequently imprecise and insufficient to distinguish among objects. In the rough set theory, this information about objects is represented by an information system (decision table). From an information system many useful facts and decision rules can be extracted, which is referred as knowledge discovery, and it is successfully applied in many fields including data mining, artificial intelligence learning or financial investment. The aim of the article is to show how hidden knowledge in the real-world data can be discovered within the rough set theory framework. After a brief preview of the rough set theory’s basic concepts, knowledge discovery is demonstrated on an example of baby car seats evaluation. For a decision rule extraction, the procedure of Ziarko and Shan is used.

PL

Teoria zbiorów przybliżonych, która powstała w roku 1980, oferuje alternatywne podejście do teorii zbiorów rozmytych, gdy ma się do czynienia ze zjawiskiem niepewności, niejasności i niekonsekwencji, często spotykanym w rzeczywistych sytuacjach. Podstawowym założeniem teorii zbiorów przybliżonych jest to, że każdy obiekt wszechświata jest związany z pewnymi informacjami, które są często nieprecyzyjne i niewystarczające do rozróżnienia między obiektami. W teorii zbiorów przybliżonych, informacje o obiektach są reprezentowane przez system informacyjny (tabela decyzyjna). System informacyjny dostarcza wiele przydatnych faktów i reguł, które są określane jako odkrywanie wiedzy, która z powodzeniem jest stosowana w wielu dziedzinach, w tym w ekstrakcji danych, sztucznej inteligencji czy przy inwestycjach finansowych. Cele artykułu jest pokazanie, w jaki sposób wiedza ukryta w rzeczywistych danych, mogą zostać odkryte w trudnych ramach teorii mnogości. Po krótkim przedstawieniu podstawowych pojęć teorii zbiorów przybliżonych, na przykładzie ocen fotelików samochodowych, przedstawiono zjawisko odkrywania wiedzy. W celu wydobycia reguły decyzyjnej zastosowano procedurę Ziarko i Shan.

7

Documents Processing in the Requirements Management System

Wydra T., Grzanek K.

Journal of Applied Computer Science Methods

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2013

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Vol. 5 No. 1

59--68

EN

Requirements analysis is a highly critical step in software life-cycle. Our solution to the problem of managing requirements is an embedded domainspecific language with Clojure playing the role of the host language. The requirements are placed directly in the source code, but there is an impedance mismatch between the compilation units and electronic documents, that are the major carriers of requirements information. This paper presents a coverage for this problem.

8

Computer models of similarity in musical pieces based on the multi-dimensional patterns

Mazurowski Ł., Wiliński A.

Journal of Applied Computer Science

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2012

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Vol. 20, nr 2

79-94

EN

This paper describes the problem of recognizing similarities in musical pieces in order to cluster and classify them with particular reference to the files stored according to the MIDI standard. The analysis of the similarity between artificially generated musical pieces to those that have been composed by a man which is carried out in order not to infringe copyrights to the existing pieces is the area of further use of the method presented. The article presents different existing methodological approaches and proposes the use of histograms of selected parameters of musical sound as a mechanism of aggregation of musical clusters potentially belonging to one group of similar musical pieces.

9

Knowledge Mining from Data: Methodological Problems and Directions for Development

Kulikowski J.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2011

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Vol. 15, No 2

227-233

EN

The development of knowledge engineering and, within its framework, of data mining or knowledge mining from data should result in the characteristics or descriptions of objects, events, processes and/or rules governing them, which should satisfy certain quality criteria: credibility, accuracy, verifiability, topicality, mutual logical consistency, usefulness, etc. Choosing suitable mathematical models of knowledge mining from data ensures satisfying only some of the above criteria. This paper presents, also in the context of the aims of The Committee on Data for Science and Technology (CODATA), more general aspects of knowledge mining and popularization, which require applying the rules that enable or facilitate controlling the quality of data.

10

Knowledge discovery in data using formal concept analysis and random projections

Aswani Kumar Ch.

International Journal of Applied Mathematics and Computer Science

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2011

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Vol. 21, no 4

745-756

EN

In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role.

11

Light Region-based Techniques for Process Discovery

Solé M., Carmona J.

Fundamenta Informaticae

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2011

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Vol. 113, nr 3/4

343-376

EN

A central problem in the area of Process Mining is to obtain a formal model that represents selected behavior of a system. The theory of regions has been applied to address this problem, enabling the derivation of a Petri net whose language includes a set of traces. However, when dealing with real-life systems, the available tool support for performing such a task is unsatisfactory, due to the complex algorithms that are required. In this paper, the theory of regions is revisited to devise a novel technique that explores the space of regions by combining the elements of a region basis. Due to its light space requirements, the approach can represent an important step for bridging the gap between the theory of regions and its industrial application. Experimental results show that there is improvement in orders of magnitude in comparison with state-of-the-art tools for the same task.

12

Accuracy of generalized context patterns in the context based sequential patterns mining

Ziembiński R. Z.

Control and Cybernetics

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2011

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Vol. 40, no 3

585-603

EN

A context pattern is a frequent subsequence mined from the context database containing set of sequences. This kind of sequential patterns and all elements inside them are described by additional sets of context attributes e.g. continuous ones. The contexts describe circumstances of transactions and sources of sequential data. These patterns can be mined by an algorithm for the context based sequential pattern mining. However, this can create large sets of patterns because all contexts related to patterns are taken from the database. The goal of the generalization method is to reduce the context pattern set by introducing a more compact and descriptive kind of patterns. This is achieved by finding clusters of similar context patterns in the mined set and transforming them to a smaller set of generalized context patterns. This process has to retain as much as possible information from the mined context patterns. This paper introduces a definition of the generalized context pattern and the related algorithm. Results from the generalization may differ as depending on the algorithm design and settings. Hence, generalized patterns may reflect frequent information from the context database differently. Thus, an accuracy measure is also proposed to evaluate the generalized patterns. This measure is used in the experiments presented. The generalized context patterns are compared to patterns mined by the basic sequential patterns mining with prediscretization of context values.

13

Acquisition of technology knowledge from online information sources

Kluska-Nawarecka S., Wilk-Kołodziejczyk D., Dziaduś-Rudnicka J., Smolarek-Grzyb A.

Archives of Foundry Engineering

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2011

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Vol. 11, iss. 3

107-112

EN

The article discusses problems related with the search of information from open sources, particularly on the Internet. Specific area of concern is searching for technical knowledge in the area of metalcasting. The results of ongoing experiments were given, to serve as a basis in identification of the opportunities to improve the process of searching through determination of own research plans.

14

Toward Intelligent Searching the Rough Set Database System (RSDS): an Ontological Approach

Suraj Z., Grochowalski P.

Fundamenta Informaticae

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2010

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Vol. 101, nr 1/2

115-123

EN

Themain goal of this paper is to give the outline of some approach to intelligent searching the Rough Set Database System (RSDS). RSDS is a bibliographical system containing bibliographical descriptions of publications connected with methodology of rough sets and its applications. The presented approach bases on created ontologies which are models for the considered domain (rough set theory, its applications and related fields) and for information about publications coming from, for example, abstracts.

15

Eksploracja danych w serwisach internetowych

Fabisiak L.

Metody Informatyki Stosowanej

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2009

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nr 3 (20)

33-42

PL

Eksploracja danych dostarcza bardzo cennej wiedzy o funkcjonowaniu serwisu. Pozwala uzyskać wiedzę o tym kto, kiedy, dlaczego i jak używa serwisu. Dzięki postępowi w komputerach i technologii rejestrowania danych, ogromne zbiory były i są gromadzone. Sztuka eksploracji danych polega na wydobyciu cennych informacji z otaczającej masy nic nie wnoszących liczb po to, żeby właściciele tych danych mogli na nich się wzbogacić. Organizację posiadają cenną, dialektyczną wiedzę o atrakcyjności swojej oferty, wiedzę o tym w jaki sposób kształtować ofertę, aby odpowiadała ona potrzebom klienta itp. Dysponując danymi uzyskanymi w procesie eksploracji można dostosować zawartość serwisów do potrzeb danego użytkownika, poprawić strukturę całego serwisu a także wprowadzić nowe elementy do serwisów internetowych. Eksploracja danych pozwala na wydzielenie grup atrakcyjnych klientów o których serwisy powinny szczególnie dbać. Eksploracja danych pozwala na pełne wykorzystanie posiadanych informacji o klientach i transakcjach, a co za tym idzie odkrycie wiedzy, która może zaważyć o losach i pozycji firmy [14]. Podsumowując, eksploracja może przynieść korzyści organizacji, gdyż dostarcza danych użytecznych w procesach podejmowania decyzji biznesowych i decyzji dotyczących funkcjonowania i rozwoju serwisu. Widoczne są też korzyści dla klienta, gdyż serwis lepiej odpowiada na jego potrzeby, a on sam częściej i chętniej korzysta z serwisu oraz jest zainteresowany jego nowymi funkcjami. Bowiem skuteczne zastosowanie eksploracji danych to takie, które przynosi ogólnie rozumiane zyski dzięki wdrożeniu jej wyników.

EN

In this work the exploration of data for analysis and evaluation of Web sites. The publication brought closer review of the methods of data mining, data mining union websites. Synthesis has been currently used in the methods and techniques.

16

The Outline of an Ontology for the Rough Set Theory and its Applications

Grochowalski P., Pancerz K.

Fundamenta Informaticae

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2009

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Vol. 93, nr 1-3

143-154

EN

The paper gives the outline of an ontology for the rough set theory and its applications. This ontology will be applied in intelligent searching the Rough Set Database System. A specialized editor from the Protege system is used to define the ontology.

17

Mining the Largest Dense Vertexlet in a Weighted Scale-free Graph

Bandyopadhyay S., Bhattacharyya M.

Fundamenta Informaticae

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2009

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Vol. 96, nr 1/2

1-25

EN

An important problem of knowledge discovery that has recently evolved in various reallife networks is identifying the largest set of vertices that are functionally associated. The topology of many real-life networks shows scale-freeness, where the vertices of the underlying graph follow a power-law degree distribution. Moreover, the graphs corresponding to most of the real-life networks are weighted in nature. In this article, the problem of finding the largest group or association of vertices that are dense (denoted as dense vertexlet) in a weighted scale-free graph is addressed. Density quantifies the degree of similarity within a group of vertices in a graph. The density of a vertexlet is defined in a novel way that ensures significant participation of all the vertices within the vertexlet. It is established that the problem is NP-complete in nature. An upper bound on the order of the largest dense vertexlet of a weighted graph, with respect to certain density threshold value, is also derived. Finally, an O(n2 log n) (n denotes the number of vertices in the graph) heuristic graph mining algorithm that produces an approximate solution for the problem is presented.

18

Wyznaczanie podzbioru atrybutów w procesie eksploracji danych

Romański P., Rosiak J., Mulawka J. J.

Elektronika : konstrukcje, technologie, zastosowania

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2009

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Vol. 50, nr 8

271-275

PL

Celem niniejszej pracy jest selekcja najlepszego podzbioru atrybutów wykorzystywanego dalej do klasyfikacji i aproksymacji w procesie eksploracji danych dokonanej przez filtrowanie atrybutów z wykorzystaniem różnych funkcji oceny przydatności. W szczególności dokonane zostanie porównanie jakości klasyfikatorów i aproksymatorów budowanych przed i po selekcji. Jako badane zbiory danych wykorzystane będą klasyczne bazy dostępne w repozytorium UCI. Do przeprowadzania oceny jakości proponowanych rozwiązań zostaną użyte klasyfikatory i aproksymatory znajdujące się w gotowych pakietach języka R.

EN

The aim of this publication is to select the best feature subsets applied further to the classification and approximation problems as part of the knowledge discovery process via filtering the attributes using different evaluation functions. In particular. a comparison of the classifiers as well as approximators' quality before and after selection will be provided. In order to test the effectiveness of the presented approaches, classic data sets available at the UCI repository will be used. The algorithms existing in the R packages will be applied to measure the quality of the proposed solutions.

19

An experimental evaluation of two approaches to mining context based sequential patterns

Stefanowski J., Ziembiński R.

Control and Cybernetics

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2009

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Vol. 38, no 1

27-45

EN

The paper discusses the results of experiments with a new context extension of a sequential pattern mining problem. In this extension, two kinds of context attributes are introduced for describing the source of a sequence and for each element inside this sequence. Such context based sequential patterns may be discovered by a new algorithm, called Context Mapping Improved, specific for handling attributes with similarity functions. For numerical attributes an alternative approach could include their pre-discretization, transforming discrete values into artificial items and, then, using an adaptation of an algorithm for mining sequential patterns from nominal items. The aim of this paper is to experimentally compare these two approaches to mine artificially generated sequence databases with numerical context attributes where several reference patterns are hidden. The results of experiments show that the Context Mapping Improved algorithm has led to better re-discovery of reference patterns. Moreover, a new measure for comparing two sets of context based patterns is introduced.

20

Toward decisional DNA: developing holistic set of experience knowledge structure

Sanin C., Szczerbicki E.

Foundations of Control and Management Sciences

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2008

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No. 9

109-122

EN

Set of Experience Knowledge Structure (SOEKS) is a structure able to collect and manage explicit knowledge of formal decision events on different forms. It was built as part of a platform for transforming information into knowledge named Knowledge Supply Chain System (KSCS). In brief, the KSCS takes information from different technologies that make formal decision events, integrates them and transforms them into knowledge represented by Sets of Experience. SOEKS is a structure that can be source and target of multiple technologies. Moreover, it comprises variables, functions, constraints and rules associated in a DNA shape allowing the construction of Decisional DNA. However, when having various dissimilar Sets of Experience as output of the same formal decision event, a renegotiation and unification of the decision has to be performed. The purpose of this paper is to show the process of renegotiating various dissimilar Sets of Experience collected from the same formal decision event.