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Tytuł:
Minig rules of concept drift using genetic algorithm
Autorzy:
Vivekanandan, P.
Nedunchezhian, R.
Powiązania:
https://bibliotekanauki.pl/articles/91705.pdf
Data publikacji:
2011
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
genetic algorithm
CDR-tree algorithm
rules
data mining
Opis:
In a database the data concepts changes over time and this phenomenon is called as concept drift. Rules of concept drift describe how the concept changes and sometimes they are interesting and mining those rules becomes more important. CDR tree algorithm is currently used to identify the rules of concept drift. Building a CDR tree becomes a complex process when the domain values of the attributes get increased. Genetic Algorithms are traditionally used for data mining tasks. In this paper, a Genetic Algorithm based approach is proposed for mining the rules of concept drift, which makes the mining task simpler and accurate when compared with the CDR-tree algorithm.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 2; 135-145
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Rough Set Application for the Tax Payer Classification Rules
Zastosowanie teorii zbiorów przybliżonych w zadaniu klasyfikacji podatników
Autorzy:
Misztal, L.
Powiązania:
https://bibliotekanauki.pl/articles/156046.pdf
Data publikacji:
2009
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
zbiory przybliżone
eksploracja danych
klasyfikacja
ekstrakcja reguł
reguły decyzyjne
rough sets
data mining
classification
rules extraction
decision rules
Opis:
Classification of the tasks for real-world problems becomes possible because of creation and use of more efficient IT systems. It also targets rough set methods as well described with solid mathematical basis for classification tasks. In the presented paper the application of rough set theory with the usage of significance of attributes and decision rule sets for classification of taxpayers is described. There are taken into account the negative or positive results of taxation control, and specific features describing payers are considered. Appropriate choice of data, building the model and its application leads to the specified goal reaching, with better accuracy in comparison to "intuitive" choice. Simultaneously it becomes possible to extract decision rules in the linguistic form, what gives opportunity for easier interpretation of obtained results. As a result of the solution application the more accurate selection of tax payers is obtained. This is of significant meaning for the tax authorities, and this leads for the better observance of the tax law.
Rozwiązywanie zadań klasyfikacji dla rzeczywistych problemów stało się możliwe dzięki rozwojowi wydajniejszych systemów informatycznych. Dotyczy to również teorii zbiorów przybliżonych dla zadań klasyfikacji. W przedstawionej publikacji zastosowano zbiory przybliżone, które mają ugruntowaną teorię bazującą na rozszerzeniu teorii zbiorów i definiującą dolne oraz górne przybliżenie, oraz wyznaczającą tabelę decyzyjną do klasyfikacji. Metodę użyto do obliczeń istotności atrybutów oraz reguł decyzyjnych opisujących klasyfikację podatników ze względu na pozytywny lub negatywny wynik kontroli, przy uwzględnieniu specyficznych cech ich opisujących. Odpowiedni dobór danych, budowa modelu oraz jego użycie umożliwiło osiągnięcia zadanego celu ze zwiększoną dokładnością w stosunku do "intuicyjnego" wyboru. Wykorzystanie zbiorów przybliżonych, które wyznaczają wyniki końcowe klasyfikacji w postaci zbioru reguł umożliwiło ich ekstrakcję w łatwo interpretowalnej formie lingwistycznej. W publikacji zastosowano autorskie rozwiązanie programowe bazujące na kolekcjach, tablicach oraz obiektach pośrednich, zaimplementowane dla bazy danych Oracle, dzięki któremu zrealizowano zadanie oraz przedstawiono rezultaty. Dzięki uzyskanym wynikom bazującym na modelu opartym na użytej metodzie możliwe staje się dokładniejsze typowanie podatników funkcjonujących w polskim systemie prawnym i mających problemy podatkowe, których należy poddać kontroli. Tym samym zwiększa się skuteczność egzekwowania prawa podatkowego.
Źródło:
Pomiary Automatyka Kontrola; 2009, R. 55, nr 10, 10; 796-798
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The use of quantitative association rules in cellular network planning
Autorzy:
Okoniewski, M.
Powiązania:
https://bibliotekanauki.pl/articles/307803.pdf
Data publikacji:
2003
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
data mining
quantitative association rules
knowledge discovery process
cellular network planning
Opis:
This paper describes the problem of planning cellular network base stations with optimization to traffic requirements. This research problem was a main incentive to add some development to the theory of association rules. The new form of quantitative and multi-dimensional association rules, unlike other approaches, does not require the discretization of real value attributes as a preprocessing step. They are discovered with data driven algorithm that gives precise and complete results and has polynomial complexity for a given dimensionality.
Źródło:
Journal of Telecommunications and Information Technology; 2003, 3; 121-124
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evaluation of the on-line commercial service quality based on association rules
Autorzy:
Szyda, Monika
Czarnowski, Ireneusz
Powiązania:
https://bibliotekanauki.pl/articles/432412.pdf
Data publikacji:
2017
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
e-commerce
e-quality evaluation
e-customer satisfaction
association rules
data mining
Opis:
The existing approaches to the evaluation of on-line commercial services quality include various quality indicators. The application of multiple attributes for quality evaluation enables and involves specialized analysis techniques to carry it out. This article proposes to utilize association rules in the quality evaluation of the on-line services. The analysis carried out of the discovered association rules has provided interesting dependencies and relationships on the individual characteristics of the on-line commercial service. On the basis of such on analysis, conclusions can be made regarding the general quality of the on-line commercial services. The discovered dependencies and connections can be used in shaping online commercial service quality. Conclusions from the analysis of the association rules can therefore be used to improve the on-line commercial service quality comprehensively which can lead to the higher satisfaction of e-customers
Źródło:
Informatyka Ekonomiczna; 2017, 1(43); 66-85
1507-3858
Pojawia się w:
Informatyka Ekonomiczna
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Data mining approach in diagnosis and treatment of chronic kidney disease
Autorzy:
Turiac, Andreea S.
Zdrodowska, Małgorzata
Powiązania:
https://bibliotekanauki.pl/articles/2105985.pdf
Data publikacji:
2022
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
feature selection
classification
classification rules
action rules
data mining
chronic kidney disease
Opis:
Chronic kidney disease is a general definition of kidney dysfunction that lasts more than 3 months. When chronic kidney disease is advanced, the kidneys are no longer able to cleanse the blood of toxins and harmful waste products and can no longer support the proper function of other organs. The disease can begin suddenly or develop latently over a long period of time without the presence of characteristic symptoms. The most common causes are other chronic diseases – diabetes and hypertension. Therefore, it is very important to diagnose the disease in early stages and opt for a suitable treatment - medication, diet and exercises to reduce its side effects. The purpose of this paper is to analyse and select those patient characteristics that may influence the prevalence of chronic kidney disease, as well as to extract classification rules and action rules that can be useful to medical professionals to efficiently and accurately diagnose patients with kidney chronic disease. The first step of the study was feature selection and evaluation of its effect on classification results. The study was repeated for four models – containing all available patient data, containing features identified by doctors as major factors in chronic kidney disease, and models containing features selected using Correlation Based Feature Selection and Chi-Square Test. Sequential Minimal Optimization and Multilayer Perceptron had the best performance for all four cases, with an average accuracy of 98.31% for SMO and 98.06% for Multilayer Perceptron, results that were confirmed by taking into consideration the F1-Score, for both algorithms was above 0.98. For all these models the classification rules are extracted. The final step was action rule extraction. The paper shows that appropriate data analysis allows for building models that can support doctors in diagnosing a disease and support their deci-sions on treatment. Action rules can be important guidelines for the doctors. They can reassure the doctor in his diagnosis or indicate new, previously unseen ways to cure the patient.
Źródło:
Acta Mechanica et Automatica; 2022, 16, 3; 180--188
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Data mining techniques as a tool in neurological disorders diagnosis
Autorzy:
Zdrodowska, M.
Dardzińska, A.
Chorąży, M.
Kułakowska, A.
Powiązania:
https://bibliotekanauki.pl/articles/386474.pdf
Data publikacji:
2018
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
data mining
classification rules
decision tree
action rules
neurological disorders
stroke
multiple sclerosis
Opis:
Neurological disorders are diseases of the brain, spine and the nerves that connect them. There are more than 600 diseases of the nervous system, such as epilepsy, Parkinson's disease, brain tumors, and stroke as well as less familiar ones such as multiple sclerosis or frontotemporal dementia. The increasing capabilities of neurotechnologies are generating massive volumes of complex data at a rapid pace. Evaluating and diagnosing disorders of the nervous system is a complicated and complex task. Many of the same or similar symptoms happen in different combinations among the different disorders. This paper provides a survey of developed selected data mining methods in the area of neurological diseases diagnosis. This review will help experts to gain an understanding of how data mining techniques can assist them in neurological diseases diagnosis and patients treatment.
Źródło:
Acta Mechanica et Automatica; 2018, 12, 3; 217-220
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Heterogeneous distance functions for prototype rules : influence of parameters on probability estimation
Autorzy:
Blachnik, M.
Duch, W.
Wieczorek, T.
Powiązania:
https://bibliotekanauki.pl/articles/92882.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
prototype rules
probability estimation
heterogeneous distance functions
similarity-based methods
classification
data mining
Opis:
An interesting and little explored way to understand data is based on prototype rules (P-rules). The goal of this approach is to find optimal similarity (or distance) functions and position of prototypes to which unknown vectors are compared. In real applications similarity functions frequently involve different types of attributes, such as continuous, discrete, binary or nominal. Heterogeneous distance functions that may handle such diverse information are usually based on probability distance measure, such as the Value Difference Metrics (VDM). For continuous attributes calculation of probabilities requires estimations of probability density functions. This process requires careful selection of several parameters that may have important impact on the overall classification of accuracy. In this paper, various heterogeneous distance function based on VDM measure are presented, among them some new heterogeneous distance functions based on different types of probability estimation. Results of many numerical experiments with such distance functions are presented on artificial and real datasets, and quite simple P-rules for several heterogeneous databases extracted.
Źródło:
Studia Informatica : systems and information technology; 2006, 1(7); 19-30
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural methods of knowledge extraction
Autorzy:
Duch, W.
Adamczak, R.
Grąbczewski, K.
Jankowski, N.
Powiązania:
https://bibliotekanauki.pl/articles/206250.pdf
Data publikacji:
2000
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
diagnostyka medyczna
optymalizacja
reguła logiczna
reguła rozmyta
wspomaganie decyzji
data mining
decision support
fuzzy rules
logical rules
medical diagnosis
optimization
Opis:
Contrary to the common opinion, neural networks may be used for knowledge extraction. Recently, a new methodology of logical rule extraction, optimization and application of rule-based systems has been described. C-MLP2LN algorithm, based on constrained multilayer perceptron network, is described here in details and the dynamics of a transition from neural to logical system illustrated. The algorithm handles real-valued features, determining appropriate linguistic variables or membership functions as a part of the rule extraction process. Initial rules are optimized by exploring the accuracy/simplicity tradeoff at the rule extraction stage and the one between reliability of rules and rejection rate at the optimization stage. Gaussian uncertainties of measurements are assumed during application of crisp logical rules, leading to "soft trapezoidal" membership functions and allowing to optimize the linguistic variables using gradient procedures. Comments are made on application of neural networks to knowledge discovery in the benchmark and real life problems.
Źródło:
Control and Cybernetics; 2000, 29, 4; 997-1017
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analiza opóźnień samolotów pasażerskich z wykorzystaniem reguł asocjacyjnych
Analysis of flights’ delays using association rules
Autorzy:
Dramski, M.
Mąka, M.
Powiązania:
https://bibliotekanauki.pl/articles/314393.pdf
Data publikacji:
2018
Wydawca:
Instytut Naukowo-Wydawniczy "SPATIUM"
Tematy:
zasady asocjacji
opóźnienia lotów
transport lotniczy
eksploracja danych
association rules
data mining
flights delays
air transport
Opis:
Wydajność transportu pasażerskiego w tym lotnictwa cywilnego, jest kluczowa dla światowej gospodarki. Jednym z głównych czynników oceny linii lotniczych przez pasażerów jest punktualność. Należy tu uwzględnić również fakt, że sieć połączeń między lotniskami na całym świecie jest niezwykle skomplikowana. Powyższe fakty prowadzą do wniosku, że można stworzyć narzędzie, które pomoże pasażerom planować ich podróż w sposób optymalny. W niniejszym artykule do analizy ponad 7 milionów lotów na terenie Stanów Zjednoczonych, zastosowano reguły asocjacyjne. Dane pozyskano z Departamentu Transportu USA i obejmują one loty, które odbyły się w 2008 roku.
The efficiency of air passenger transport in world's economy is crucial. For this kind of flights, one of the most important features is punctuality. The network of connections between the airports, very often is significantly complicated. It leads to the conclusion that there is a need to do some research in this field which will help the passengers to plan their optimal journeys. In this paper one of the data mining techniques (association rules) was applied to the analysis of flights' delays. The data consists of over 7 millions records was taken from the US Department of Transportation (year 2008) [2]. Then the research was carried out and conclusions were described.
Źródło:
Autobusy : technika, eksploatacja, systemy transportowe; 2018, 19, 12; 755-757
1509-5878
2450-7725
Pojawia się w:
Autobusy : technika, eksploatacja, systemy transportowe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment
Autorzy:
Del Gallo, Mateo
Ciarapica, Filippo Emanuele
Mazzuto, Giovanni
Bevilacqua, Maurizio
Powiązania:
https://bibliotekanauki.pl/articles/27324213.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
data mining
association rules
optimization model
production scheduling
job-shop scheduling
flow shop scheduling
Opis:
Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied.
Źródło:
Management and Production Engineering Review; 2023, 14, 4; 56--70
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Selected morphotic parameters differentiating ulcerative colitis from Crohn’s disease
Autorzy:
Kasperczuk, Anna
Powiązania:
https://bibliotekanauki.pl/articles/2112830.pdf
Data publikacji:
2021
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
action rules
data mining
colon disease
morphotic parameters
smoking
Opis:
This paper presents a method that binds statistical and data mining techniques, which aims to support the decision-making process in selected diseases of the digestive system. Currently, there is no precise diagnosis for ulcerative colitis (UC) and Crohn's disease (CD). Specialist physicians must exclude many other diseases occurring in the colon. The first goal of this study is a retrospective analysis of medical data of patients hospitalised in the Department of Gastroenterology and Internal Diseases, Bialystok, and finding the symptoms differentiating the two analysed diseases. The second goal is to build a system that clearly points to one of the two diseases UC or CD, which shortens the time of diagnosis and facilitates the future treatment of patients. The work focuses on building a model that can be the basis for the construction of action rules, which are one of the basic elements in the medical recommendation system. Generated action rules indicated differentiating factors, such as mean corpuscular volume, platelets (PLTs), neutrophils, monocytes, eosinophils, basophils, alanine aminotransferase (ALAT), creatinine, sodium and potassium. Other important parameters were smoking and blood in stool.
Źródło:
Acta Mechanica et Automatica; 2021, 15, 4; 249--253
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Algorithm CFP-SFPwith parallel processing
Autorzy:
Kujawiak, M.
Powiązania:
https://bibliotekanauki.pl/articles/92930.pdf
Data publikacji:
2008
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
association rules
data mining
web logs
a priori
a priori TID
a priori hybrid algorithm
FP-Tree
Opis:
Existing algorithms for finding association rules do not implement parallel processing. This paper proposes CFP-SFP (Creating Frequent Patterns with Set from Frequent Patterns) algorithm with parallel processing. The research involves running CEP-SEP algorithm with one thread and a dozen or so threads that are executed simultaneously. The research was conducted on a computer with one processor and dual-core processor.
Źródło:
Studia Informatica : systems and information technology; 2008, 1(10); 87-93
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on operation fault diagnosis algorithm of power grid equipment based on power big data
Autorzy:
Qian, Jianguo
Zhu, Bingquan
Li, Ying
Shi, Zhengchai
Powiązania:
https://bibliotekanauki.pl/articles/949910.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
association rules
big data
data mining
fault diagnosis
grid equipment
Opis:
Power big data contains a lot of information related to equipment fault. The analysis and processing of power big data can realize fault diagnosis. This study mainly analyzed the application of association rules in power big data processing. Firstly, the association rules and the Apriori algorithm were introduced. Then, aiming at the shortage of the Apriori algorithm, an IM-Apriori algorithm was designed, and a simulation experiment was carried out. The results showed that the IM-Apriori algorithm had a significant advantage over the Apriori algorithm in the running time. When the number of transactions was 100 000, the running of the IM-Apriori algorithm was 38.42% faster than that of the Apriori algorithm. The IM-Apriori algorithm was little affected by the value of supportmin. Compared with the Extreme Learning Machine (ELM), the IM-Apriori algorithm had better accuracy. The experimental results show the effectiveness of the IM-Apriori algorithm in fault diagnosis, and it can be further promoted and applied in power grid equipment.
Źródło:
Archives of Electrical Engineering; 2020, 69, 4; 793-800
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative evaluation of the different data mining techniques used for the medical database
Autorzy:
Kasperczuk, A.
Dardzińska, A.
Powiązania:
https://bibliotekanauki.pl/articles/386432.pdf
Data publikacji:
2016
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
data mining
classification
WEKA
J48
MLP
apriori
association rules
baza wiedzy medycznej
eksploracja danych
algorytm klasyfikacji
Opis:
Data mining is the upcoming research area to solve various problems. Classification and finding association are two main steps in the field of data mining. In this paper, we use three classification algorithms: J48 (an open source Java implementation of C4.5 algorithm), Multilayer Perceptron - MLP (a modification of the standard linear perceptron) and Naïve Bayes (based on Bayes rule and a set of conditional independence assumptions) of the Weka interface. These classifiers have been used to choose the best algorithm based on the conditions of the voice disorders database. To find association rules over transactional medical database first we use apriori algorithm for frequent item set mining. These two initial steps of analysis will help to create the medical knowledgebase. The ultimate goal is to build a model, which can improve the way to read and interpret the existing data in medical database and future data as well.
Źródło:
Acta Mechanica et Automatica; 2016, 10, 3; 233-238
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applying Rough Set Theory for the Modeling of Austempered Ductile Iron Properties
Autorzy:
Kochański, A.
Soroczyński, A.
Kozłowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/382215.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
data mining
knowledge rules
rough set theory
casting
pozyskiwanie danych
reguły wiedzy
teoria zbiorów przybliżonych
odlewnictwo
Opis:
The article discusses the possibilities of employing an algorithm based on the Rough Set Theory for generating engineering knowledge in the form of logic rules. The logic rules were generated from the data set characterizing the influence of process parameters on the ultimate tensile strength of austempered ductile iron. The paper assesses the obtained logic rules with the help of the rule quality evaluation measures, that is, with the help of the measures of confidence, support, and coverage, as well as the proposed rule quality coefficient.
Źródło:
Archives of Foundry Engineering; 2013, 13, 2 spec.; 70-73
1897-3310
2299-2944
Pojawia się w:
Archives of Foundry Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
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