Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "mining data" wg kryterium: Temat

  Lista filtrów

Dostawca treści

Podbaza

Wyrównaj
Wyświetlanie 1-15 z 285
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:
A complete gradient clustering algorithm formed with kernel estimators
Autorzy:
Kulczycki, P.
Charytanowicz, M.
Powiązania:
https://bibliotekanauki.pl/articles/907781.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
analiza danych
eksploracja danych
grupowanie
metoda statystyczna
estymacja jądrowa
obliczenia numeryczne
data analysis
data mining
clustering
gradient procedures
nonparametric statistical methods
kernel estimators
numerical calculations
Opis:
The aim of this paper is to provide a gradient clustering algorithm in its complete form, suitable for direct use without requiring a deeper statistical knowledge. The values of all parameters are effectively calculated using optimizing procedures. Moreover, an illustrative analysis of the meaning of particular parameters is shown, followed by the effects resulting from possible modifications with respect to their primarily assigned optimal values. The proposed algorithm does not demand strict assumptions regarding the desired number of clusters, which allows the obtained number to be better suited to a real data structure. Moreover, a feature specific to it is the possibility to influence the proportion between the number of clusters in areas where data elements are dense as opposed to their sparse regions. Finally, the algorithm-by the detection of one-element clusters-allows identifying atypical elements, which enables their elimination or possible designation to bigger clusters, thus increasing the homogeneity of the data set.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2010, 20, 1; 123-134
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A computer-based system for objective studies of human psychomotorics
Autorzy:
Ossowski, A.
Smyrnova, J.
Powiązania:
https://bibliotekanauki.pl/articles/332896.pdf
Data publikacji:
2002
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
test psychologiczny
psychoakustyki
czas reakcji
eksploracja danych
system komputerowy
psychological test
psychomotorics
psychoacoustics
reaction time
data mining
computer system
Opis:
Firstly the idea of objective psychological tests and their characteristics related to various features of human psychophysiology are introduced. Examples of objective tests are given. Next application of data mining algorithms to analyse data obtained from different tests are outlined. The general concept of a computer system for objective psychological studies of psychomotoric processes in humans is then described. Finally the possibility of implementation of the system in medical and psychoacoustical studies is pointed out.
Źródło:
Journal of Medical Informatics & Technologies; 2002, 3; MI71-79
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Data Mining Approach for Analysis of a Wire Electrical Discharge Machining Process
Autorzy:
Dandge, Shruti Sudhakar
Chakraborty, Shankar
Powiązania:
https://bibliotekanauki.pl/articles/2023974.pdf
Data publikacji:
2021-09
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
wire electrical discharge machining
data mining
classification and regression tree
chi-squared
automatic interaction detection
classification
Opis:
Wire electrical discharge machining (WEDM) is a non-conventional material-removal process where a continuously travelling electrically conductive wire is used as an electrode to erode material from a workpiece. To explore its fullest machining potential, there is always a requirement to examine the effects of its varied input parameters on the responses and resolve the best parametric setting. This paper proposes parametric analysis of a WEDM process by applying non-parametric decision tree algorithm, based on a past experimental dataset. Two decision tree-based classification methods, i.e. classification and regression tree (CART) and Chi-squared automatic interaction detection (CHAID) are considered here as the data mining tools to examine the influences of six WEDM process parameters on four responses, and identify the most preferred parametric mix to help in achieving the desired response values. The developed decision trees recognize pulse-on time as the most indicative WEDM process parameter impacting almost all the responses. Furthermore, a comparative analysis on the classification performance of CART and CHAID algorithms demonstrates the superiority of CART with higher overall classification accuracy and lower prediction risk.
Źródło:
Management and Production Engineering Review; 2021, 13, 3; 116-128
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A data mining approach to improve military demand forecasting
Autorzy:
Thiagarajan, R.
Rahman, M.
Gossink, N.
Calbert, G.
Powiązania:
https://bibliotekanauki.pl/articles/91684.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
critical stocks
demand
forecasting
military operation
military planning
military supplies
autocorrelated
cross-correlated
data mining
Opis:
Accurately forecasting the demand of critical stocks is a vital step in the planning of a military operation. Demand prediction techniques, particularly autocorrelated models, have been adopted in the military planning process because a large number of stocks in the military inventory do not have consumption and usage rates per platform (e.g., ship). However, if an impending military operation is (significantly) different from prior campaigns then these prediction models may under or over estimate the demand of critical stocks leading to undesired operational impacts. To address this, we propose an approach to improve the accuracy of demand predictions by combining autocorrelated predictions with cross-correlated demands of items having known per-platform usage rates. We adopt a data mining approach using sequence rule mining to automatically determine crosscorrelated demands by assessing frequently co-occurring usage patterns. Our experiments using a military operational planning system indicate a considerable reduction in the prediction errors across several categories of military supplies.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 3; 205-214
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Deep-Learning-Based Bug Priority Prediction Using RNN-LSTM Neural Networks
Autorzy:
Bani-Salameh, Hani
Sallam, Mohammed
Al shboul, Bashar
Powiązania:
https://bibliotekanauki.pl/articles/1818480.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
assigning
priority
bug tracking systems
bug priority
bug severity
closed-source
data mining
machine learning
ML
deep learning
RNN-LSTM
SVM
KNN
Opis:
Context: Predicting the priority of bug reports is an important activity in software maintenance. Bug priority refers to the order in which a bug or defect should be resolved. A huge number of bug reports are submitted every day. Manual filtering of bug reports and assigning priority to each report is a heavy process, which requires time, resources, and expertise. In many cases mistakes happen when priority is assigned manually, which prevents the developers from finishing their tasks, fixing bugs, and improve the quality. Objective: Bugs are widespread and there is a noticeable increase in the number of bug reports that are submitted by the users and teams’ members with the presence of limited resources, which raises the fact that there is a need for a model that focuses on detecting the priority of bug reports, and allows developers to find the highest priority bug reports. This paper presents a model that focuses on predicting and assigning a priority level (high or low) for each bug report. Method: This model considers a set of factors (indicators) such as component name, summary, assignee, and reporter that possibly affect the priority level of a bug report. The factors are extracted as features from a dataset built using bug reports that are taken from closed-source projects stored in the JIRA bug tracking system, which are used then to train and test the framework. Also, this work presents a tool that helps developers to assign a priority level for the bug report automatically and based on the LSTM’s model prediction. Results: Our experiments consisted of applying a 5-layer deep learning RNN-LSTM neural network and comparing the results with Support Vector Machine (SVM) and K-nearest neighbors (KNN) to predict the priority of bug reports. The performance of the proposed RNN-LSTM model has been analyzed over the JIRA dataset with more than 2000 bug reports. The proposed model has been found 90% accurate in comparison with KNN (74%) and SVM (87%). On average, RNN-LSTM improves the F-measure by 3% compared to SVM and 15.2% compared to KNN. Conclusion: It concluded that LSTM predicts and assigns the priority of the bug more accurately and effectively than the other ML algorithms (KNN and SVM). LSTM significantly improves the average F-measure in comparison to the other classifiers. The study showed that LSTM reported the best performance results based on all performance measures (Accuracy = 0.908, AUC = 0.95, F-measure = 0.892).
Źródło:
e-Informatica Software Engineering Journal; 2021, 15, 1; 29--45
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A framework for event based modeling and analysis
Autorzy:
Granat, J.
Powiązania:
https://bibliotekanauki.pl/articles/308870.pdf
Data publikacji:
2006
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
event mining
temporal data mining
telecommunications
Opis:
In this paper we will present a framework for modeling and management of complex systems. There are various approaches for modeling of these systems. One of the approaches is events driven modeling and management of complex system. Such approach is needed in information systems that provide information in real-time. Most of the existing modeling approaches use only information about type of event and the time when an event occurs. However, in the databases we can store and then we can use much richer information about events. This information might be structured as well as unstructured. There are new challenges in algorithms development in case of description of event by various attributes.
Źródło:
Journal of Telecommunications and Information Technology; 2006, 4; 88-90
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Fuzzy Logic Based Approach to Linguistic Summaries of Databases
Autorzy:
Kacprzyk, J.
Yager, R. R.
Zadrożny, S.
Powiązania:
https://bibliotekanauki.pl/articles/911154.pdf
Data publikacji:
2000
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
logika rozmyta
baza danych
podsumowanie lingwistyczne
zgłębianie danych
fuzzy logic
linguistic summary
computing with words
data mining
fuzzy querying
Opis:
In this paper, we present basic ideas and perspectives related to the use of fuzzy logic for the derivation of linguistic summaries of data (databases). We concentrate on the issue of how to measure the goodness of a linguistic summary, and on how to embed data summarization within the fuzzy querying environment, for an effective and efficient implementation. In particular, we propose how to efficiently implement Kacprzyk and Yager's (2000) new quality indicators of linguistic summaries to derive summaries via Kacprzyk and Zadrozny's (1994; 1995a; 1995b; 1996) fuzzy querying add-on. Finally, we present an implementation for deriving linguistic summaries of a sales database at a computer retailer, and show how the linguistic summaries obtained can be useful for supporting decisions of the business owner.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 813-834
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hierarchical preference voting system for mining method selection problem
Wykorzystanie systemu głosowania zakładający hierarchię preferencji przy wyborze odpowiedniej metody wybierania
Autorzy:
Nourali, H.
Nourali, S.
Ataei, M.
Imanipour, N.
Powiązania:
https://bibliotekanauki.pl/articles/219350.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
wybór metody wybierania
procesy decyzyjne
preferencyjny system głosowania
metoda obwiedni danych
mining method selection
Multi Attribute Decision Making (MADM)
preference voting system
data envelopment analysis
Opis:
To apply decision making theory for Mining Method Selection (MMS) problem, researchers have faced two difficulties in recent years: (i) calculation of relative weight for each criterion, (ii) uncertainty in judgment for decision makers. In order to avoid these difficulties, we apply a Hierarchical Preference Voting System (HPVS) for MMS problem that uses a Data Envelopment Analysis (DEA) model to produce weights associated with each ranking place. The presented method solves the problem in two stages. In the first stage, weights of criteria are calculated and at the second stage, alternatives are ranked with respect to all criteria. A simple case study has also been presented to illustrate the competence of this method. The results show that this approach reduces some difficulties of previous methods and could be applied simply in group decision making with too many decision makers and criteria. Also, regarding to application of a mathematical model, subjectivity is reduced and outcomes are more reliable.
Przy wykorzystywaniu teorii decyzyjnych do zagadnień związanych z wyborem właściwej metody wybierania, badacze na przestrzeni lat napotykali na dwie zasadnicze trudności: (i) obliczenie odpowiedniego współczynnika wagi dla poszczególnych kryteriów oraz (ii) niepewność osądów dokonywanych przez decydentów. W celu uniknięcia tych trudności, zastosowaliśmy system głosowania zakładający hierarchię preferencji przy podejmowaniu decyzji odnośnie wyboru metody wybierania. W tym celu wykorzystano model DEA (metoda obwiedni danych) dla wygenerowania wag związanych z poszczególnymi pozycjami w rankingu. Proponowana metoda zakłada rozwiązanie problemu w dwóch etapach. W pierwszym etapie obliczane są wagi przyporządkowane poszczególnym kryteriom, w etapie drugim przeprowadzany jest ranking rozwiązań alternatywnych w odniesieniu do wszystkich kryteriów. Przedstawiono proste studium przypadku dla zilustrowania działania metody. Wyniki wskazują, że zastosowane podejście redukuje pewne niedogodności związane z poprzednio stosowanymi metodami i może być z powodzeniem wykorzystane do podejmowania decyzji grupowych, w sytuacjach gdy mamy do czynienia z wieloma decydentami i wieloma kryteriami. Ponadto, zastosowanie modelu matematycznego pozwala na ograniczenie subiektywizmu w ocenie, dzięki temu wyniki są bardziej wiarygodne.
Źródło:
Archives of Mining Sciences; 2012, 57, 4; 1056-1070
0860-7001
Pojawia się w:
Archives of Mining Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A k-Nearest Neighbors Method for Classifying User Sessions in E-Commerce Scenario
Autorzy:
Suchacka, G.
Skolimowska-Kulig, M.
Potempa, A.
Powiązania:
https://bibliotekanauki.pl/articles/308645.pdf
Data publikacji:
2015
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
data mining
e-commerce
k-Nearest Neighbors
k-NN
log file analysis
online store
R-project
supervised classification
web mining
Web store
Web traffic
Web usage mining
Opis:
This paper addresses the problem of classification of user sessions in an online store into two classes: buying sessions (during which a purchase confirmation occurs) and browsing sessions. As interactions connected with a purchase confirmation are typically completed at the end of user sessions, some information describing active sessions may be observed and used to assess the probability of making a purchase. The authors formulate the problem of predicting buying sessions in a Web store as a supervised classification problem where there are two target classes, connected with the fact of finalizing a purchase transaction in session or not, and a feature vector containing some variables describing user sessions. The presented approach uses the k-Nearest Neighbors (k-NN) classification. Based on historical data obtained from online bookstore log files a k-NN classifier was built and its efficiency was verified for different neighborhood sizes. A 11-NN classifier was the most effective both in terms of buying session predictions and overall predictions, achieving sensitivity of 87.5% and accuracy of 99.85%.
Źródło:
Journal of Telecommunications and Information Technology; 2015, 3; 64-69
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Key-Finding Algorithm Based on Music Signature
Autorzy:
Kania, Dariusz
Kania, Paulina
Powiązania:
https://bibliotekanauki.pl/articles/177312.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
music information retrieval
computational music cognition
music data mining
music visualisation
Opis:
The paper presents the key-finding algorithm based on the music signature concept. The proposed music signature is a set of 2-D vectors which can be treated as a compressed form of representation of a musical content in the 2-D space. Each vector represents different pitch class. Its direction is determined by the position of the corresponding major key in the circle of fifths. The length of each vector reflects the multiplicity (i.e. number of occurrences) of the pitch class in a musical piece or its fragment. The paper presents the theoretical background, examples explaining the essence of the idea and the results of the conducted tests which confirm the effectiveness of the proposed algorithm for finding the key based on the analysis of the music signature. The developed method was compared with the key-finding algorithms using Krumhansl-Kessler, Temperley and Albrecht-Shanahan profiles. The experiments were performer on the set of Bach preludes, Bach fugues and Chopin preludes.
Źródło:
Archives of Acoustics; 2019, 44, 3; 447-457
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Method to Make Classification of the Heat Treatment Processes Performed on Bronze Using Incomplete Knowledge
Autorzy:
Kluska-Nawarecka, S.
Górny, Z.
Regulski, K.
Wilk-Kołodziejczyk, D.
Jančíková, Z.
David, J.
Powiązania:
https://bibliotekanauki.pl/articles/947501.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
information technology
foundry industry
heat treatment
classification algorithms
rough sets
data mining
technologia informacyjna
przemysł odlewniczy
obróbka cieplna
algorytmy klasyfikacyjne
zbiory przybliżone
Opis:
The article describes the problem of selection of heat treatment parameters to obtain the required mechanical properties in heat- treated bronzes. A methodology for the construction of a classification model based on rough set theory is presented. A model of this type allows the construction of inference rules also in the case when our knowledge of the existing phenomena is incomplete, and this is situation commonly encountered when new materials enter the market. In the case of new test materials, such as the grade of bronze described in this article, we still lack full knowledge and the choice of heat treatment parameters is based on a fragmentary knowledge resulting from experimental studies. The measurement results can be useful in building of a model, this model, however, cannot be deterministic, but can only approximate the stochastic nature of phenomena. The use of rough set theory allows for efficient inference also in areas that are not yet fully explored.
Źródło:
Archives of Foundry Engineering; 2014, 14, 2; 69-72
1897-3310
2299-2944
Pojawia się w:
Archives of Foundry Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Multi-label Transformation Framework for the Rectangular 2D Strip-Packing Problem
Autorzy:
Neuenfeldt Júnior, Alvaro
Francescatto, Matheus
Stieler, Gabriel
Disconzi, David
Powiązania:
https://bibliotekanauki.pl/articles/2023851.pdf
Data publikacji:
2021-12
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
strip packing problem
data mining
multi-label transformation
classification analysis
heuristics
Opis:
The present paper describes a methodological framework developed to select a multi-label dataset transformation method in the context of supervised machine learning techniques. We explore the rectangular 2D strip-packing problem (2D-SPP), widely applied in industrial processes to cut sheet metals and paper rolls, where high-quality solutions can be found for more than one improvement heuristic, generating instances with multi-label behavior. To obtain single-label datasets, a total of five multi-label transformation methods are explored. 1000 instances were generated to represent different 2D-SPP variations found in real-world applications, labels for each instance represented by improvement heuristics were calculated, along with 19 predictors provided by problem characteristics. Finally, classification models were fitted to verify the accuracy of each multi-label transformation method. For the 2D-SPP, the single-label obtained using the exclusion method fit more accurate classification models compared to the other four multi-label transformation methods adopted.
Źródło:
Management and Production Engineering Review; 2021, 14, 4; 27-37
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new data mining approach for power performance verification of an on-shore wind farm
Autorzy:
Castellani, F.
Garinei, A.
Terzi, L.
Astolfi, D.
Moretti, M.
Lombardi, A.
Powiązania:
https://bibliotekanauki.pl/articles/328750.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
wind energy
renewable energy
wind turbine performance
data mining
SCADA database
control systems
fault diagnosis
performance optimization
wind turbine power output
Opis:
Monitoring wind energy production is fundamental to improve the performances of a wind farm during the operational phase. In order to perform reliable operational analysis, data mining of all available information spreading out from turbine control systems is required. In this work a SCADA (Supervisory Control And Data Acquisition) data analysis was performed on a small wind farm and new post-processing methods are proposed for condition monitoring of the aerogenerators. Indicators are defined to detect the malfunctioning of a wind turbine and to select meaningful data to investigate the causes of the anomalous behaviour of a turbine. The operating state database is used to collect information about the proper power production of a wind turbine and a number map has been codified for converting the performance analysis problem into a purely numerical one. Statistical analysis on the number map clearly helps in detecting operational anomalies, providing diagnosis for their reasons. The most operationally stressed turbines are systematically detected through the proposal of two Malfunctioning Indices. Results demonstrate that a proper selection of the SCADA data can be very useful to measure the real performances of a wind farm and thus to define optimal repair/replacement and preventive maintenance policies that play a major role in case of energy production.
Źródło:
Diagnostyka; 2013, 14, 4; 35-42
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new method for automatic determining of the DBSCAN parameters
Autorzy:
Starczewski, Artur
Goetzen, Piotr
Er, Meng Joo
Powiązania:
https://bibliotekanauki.pl/articles/1837535.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
clustering algorithms
DBSCAN
data mining
Opis:
Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the eps radius and the MinPts density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k-th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 3; 209-221
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 285

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies