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Wyświetlanie 1-15 z 196
Tytuł:
Metody eksploracji danych i ich zastosowanie
Data mining methods and their applications
Autorzy:
Racka, Katarzyna
Powiązania:
https://bibliotekanauki.pl/articles/446781.pdf
Data publikacji:
2015
Wydawca:
Mazowiecka Uczelnia Publiczna w Płocku
Tematy:
data mining
data mining methods
examples of data mining methods applications
data mining software
Opis:
Success in the financial market reach those companies that having fast access to data can it properly used. In modern databases and data warehouse are collected vast amounts of information, which man himself is not able to quickly analyze. For this purpose are used the data mining methods that enable the discovery of new knowledge, that is, rules, patterns and relationships in large databases. The aim of this article is to present the data mining methods and their applications. Article is divided into two parts. In the first part of the article explains the concept of data mining and data mining methods are discussed and provides examples of their applications. In the second part of the article presents the companies selling on the Polish market commercial data mining software and examples od free open-source data mining software are discussed.
Źródło:
Zeszyty Naukowe PWSZ w Płocku. Nauki Ekonomiczne; 2015, 1(21); 143-150
1644-888X
Pojawia się w:
Zeszyty Naukowe PWSZ w Płocku. Nauki Ekonomiczne
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Integration of candidate hash trees in concurrent processing of frequent itemset queries using Apriori
Autorzy:
Grudziński, P.
Wojciechowski, M.
Powiązania:
https://bibliotekanauki.pl/articles/970835.pdf
Data publikacji:
2009
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
data mining
frequent itemset mining
data mining queries
Opis:
Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. In this paper we address the problem of processing batches of frequent itemset queries using the Apriori algorithm. The best solution of this problem proposed so far is Common Counting, which consists in concurrent execution of the queries using Apriori with the integration of scans of the parts of the database shared among the queries. In this paper we propose a new method - Common Candidate Tree, offering a more tight integration of the concurrently processed queries by sharing memory data structures, i.e., candidate hash trees. The experiments show that Common Candidate Tree outperforms Common Counting in terms of execution time. Moreover, thanks to smaller memory consumption, Common Candidate Tree can be applied to larger batches of queries.
Źródło:
Control and Cybernetics; 2009, 38, 1; 47-65
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Big Data Mining Approach For Finding Top Rated URL
Autorzy:
Shyam Mohan, J. S.
Shanmugapriya, P.
Kumar, Bhamidipati Vinay Pawan
Powiązania:
https://bibliotekanauki.pl/articles/108631.pdf
Data publikacji:
2015
Wydawca:
Społeczna Akademia Nauk w Łodzi
Tematy:
big data
data mining
Opis:
Finding out the widely used URL’s from online shopping sites for any particular category is a difficult task as there are many heterogeneous and multi-dimensional data set which depends on various factors. Traditional data mining methods are limited to homogenous data source, so they fail to sufficiently consider the characteristics of heterogeneous data. This paper presents a consistent Big Data mining search which performs analytics on text data to find the top rated URL’s. Though many heuristic search methods are available, our proposed method solves the problem of searching compared with traditional methods in data mining. The sample results are obtained in optimal time and are compared with other methods which is effective and efficient.
Źródło:
Journal of Applied Computer Science Methods; 2015, 7 No. 1; 17-32
1689-9636
Pojawia się w:
Journal of Applied Computer Science Methods
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Przegląd i klasyfikacja zastosowań, metod oraz technik eksploracji danych
Data mining review and use’s classification, methods and techniques
Autorzy:
Mirończuk, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/41204012.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Kazimierza Wielkiego w Bydgoszczy
Tematy:
techniki eksploracji danych
zastosowania eksploracji danych
ED
eksploracja danych
metody eksploracji danych
data mining
data mining methods
data mining techniques
data mining classification
data minig review
Opis:
Wzrost ilości danych jak i informacji w aktualnych systemach informacyjnych wymusił powstanie nowych procesów oraz technik i metod do ich składowania, przetwarzania oraz analizowania. Do analizy dużych zbiorów danych aktualnie wykorzystuje się osiągnięcia z obszaru analizy statystycznej oraz sztucznej inteligencji (ang. artificial intelligence). Dziedziny te wykorzystane w ramach procesu analizy dużych ilości danych stanowią rdzeń eksploracji danych. Aktualnie eksploracja danych pretenduje do stania się samodzielną metodą naukową wykorzystywaną do rozwiązywania problemów analizy informacji pochodzących m.in. z systemów ich zarządzania. W niniejszym artykule dokonano przeglądu i klasyfikacji zastosowań oraz metod i technik wykorzystywanych podczas procesu eksploracji danych. Dokonano w nim także omówienia aktualnych kierunków rozwoju i elementów składających się na tą młodą stosowaną dziedzinę nauki.
The large quantity of the data and information accumulated into actual information systems and their successive extension extorted the development of new processes, techniques and methods to their storing, processing and analysing. Currently the achievement from the statistical analyses and artificial intelligence area are use to the analysis process of the large data sets. These fields make up the core of data exploration - data mining. Currently the data mining aspires to independent scientific method which one uses to solving problems from range of information analysis comes from the data bases menagments systems. In this article was described review and use's classification, methods and techniques which they are using in the process of the data exploration. In this article also was described actual development direction and described elements which require this young applied discipline of the science.
Źródło:
Studia i Materiały Informatyki Stosowanej; 2010, 2; 35-46
1689-6300
Pojawia się w:
Studia i Materiały Informatyki Stosowanej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combining classifiers for foreign pattern rejectionCombining classifiers for foreign pattern rejection
Autorzy:
Homenda, Władysław
Jastrzębska, Agnieszka
Pedrycz, Witold
Yu, Fusheng
Powiązania:
https://bibliotekanauki.pl/articles/1837475.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
data mining
knowledge engineering
Opis:
In this paper, we look closely at the issue of contaminated data sets, where apart from legitimate (proper) patterns we encounter erroneous patterns. In a typical scenario, the classification of a contaminated data set is always negatively influenced by garbage patterns (referred to as foreign patterns). Ideally, we would like to remove them from the data set entirely. The paper is devoted to comparison and analysis of three different models capable to perform classification of proper patterns with rejection of foreign patterns. It should be stressed that the studied models are constructed using proper patterns only, and no knowledge about thecharacteristics of foreign patterns is needed. The methods are illustrated with a case study of handwritten digits recognition, but the proposed approach itself is formulated in a general manner. Therefore, it can be applied to different problems. We have distinguished three structures: global, local, and embedded, all capable to eliminate foreign patterns while performing classification of proper patterns at the same time. A comparison of the proposed models shows that the embedded structure provides the best results but at the cost of a relatively high model complexity. The local architecture provides satisfying results and at the same time is relatively simple.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 75-94
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modeling preparation for data mining processes
Autorzy:
Eule, T.
Powiązania:
https://bibliotekanauki.pl/articles/308872.pdf
Data publikacji:
2006
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
data mining
data preparation
KDD process
Opis:
Today many different software tools for decision support exist; the same is true for data mining which can be seen as a particularly challenging sub-area of decision support. Choosing the most suitable tool for a particular industrial data mining application is becoming difficult, especially for industrial decision makers whose expertise is in a different field. This paper provides a conceptual analysis of crucial features of current data mining software tools, by establishing an abstract view on typical processes in data mining. Thus a common terminology is given which simplifies the comparison of tools. Based on this analysis, objective decisions for the application of decision supporting software tools in industrial practice can be made.
Źródło:
Journal of Telecommunications and Information Technology; 2006, 4; 81-87
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using advanced data mining and integration in environmental prediction scenarios
Autorzy:
Habala, O.
Hluchy, L.
Tran, V.
Krammer, P.
Seleng, M.
Powiązania:
https://bibliotekanauki.pl/articles/305607.pdf
Data publikacji:
2012
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
data mining
data integration
meteorology
hydrology
Opis:
We present one of the meteorological and hydrological experiments performed in the FP7 project ADMIRE. It serves as an experimental platform for hydrologists, and we have used it also as a testing platform for a suite of advanced data integration and data mining (DMI) tools, developed within ADMIRE. The idea of ADMIRE is to develop an advanced DMI platform accessible even to users who are not familiar with data mining techniques. To this end, we have designed a novel DMI architecture, supported by a set of software tools, managed by DMI process descriptions written in a specialized high-level DMI language called DISPEL, and controlled via several different user interfaces, each performing a different set of tasks and targeting different user group.
Źródło:
Computer Science; 2012, 13 (1); 5-16
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Towards human consistent data driven decision support systems using verbalization of data mining results via linguistic data summaries
Autorzy:
Kacprzyk, J.
Szadrozny, S.
Powiązania:
https://bibliotekanauki.pl/articles/200700.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
decision support system
data mining
Opis:
We present how the conceptually and numerically simple concept of a fuzzy linguistic database summary can be a very powerful tool for gaining much insight into the essence of data that may be relevant for a business activity. The use of linguistic summaries provides tools for the verbalization of data analysis (mining) results which, in addition to the more commonly used visualization e.g. via a GUI, graphical user interface, can contribute to an increased human consistency and ease of use. The results (knowledge) derived are in a simple, easily comprehensible linguistic form which can be effectively and efficiently employed for supporting decision makers via the data driven decision support system paradigm. Two new relevant aspects of the analysis are also outlined which was first initiated by the authors. First, following Kacprzyk and Zadrożny [1] comments are given on an extremely relevant aspect of scalability of linguistic summarization of data, using their new concept of a conceptual scalability that is crucial for large applications. Second, following Kacprzyk and Zadrożny [2] it is further considered how linguistic data summarization is closely related to some types of solutions used in natural language generation (NLG), which can make it possible to use more and more effective and efficient tools and techniques developed in this another rapidly developing area. An application of a computer retailer is outlined.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2010, 58, 3; 359-370
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
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
Tytuł:
Knowledge mining from data: methodological problems and directions for development
Autorzy:
Kulikowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/1934003.pdf
Data publikacji:
2011
Wydawca:
Politechnika Gdańska
Tematy:
data mining
knowledge discovery
data quality
CODATA
Opis:
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.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2011, 15, 2; 227-233
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Briefly on the GUHA method of data mining
Autorzy:
Hajek, P.
Powiązania:
https://bibliotekanauki.pl/articles/307807.pdf
Data publikacji:
2003
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
GUHA method
data mining
exploratory data analysis
Opis:
The paper gives brief, user-oriented, information on the GUHA method.
Źródło:
Journal of Telecommunications and Information Technology; 2003, 3; 112-114
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analytical intelligence tools for multicriterial diagnostics of CNC machines
Autorzy:
Kuric, I.
Zajačko, I.
Císar, M.
Powiązania:
https://bibliotekanauki.pl/articles/957950.pdf
Data publikacji:
2016
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
multicriterions diagnostics
analytical intelligence
data mining
Opis:
Analytical Intelligence is a set of methods and tools for acquisition and transformation of raw data into meaningful and useful information. Multicriterial diagnostics is an approach to obtain a real status of machining process just in time and produce a big pile of raw data. The paper presents a scheme of utilisation of analytical intelligence tools in multicriterial diagnostic of CNC machine tools. It is an effort to obtain a complex perception about all influences represented with measured data on machine precision.
Źródło:
Advances in Science and Technology. Research Journal; 2016, 10, 32; 59-64
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Knowledge discovery from an ERP database in the context of new product development
Autorzy:
Relich University of Zielona Gora, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/431881.pdf
Data publikacji:
2013
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
knowledge management
project management
data selection
data mining
Opis:
This paper is aimed at using an ERP database to identify the variables that have a significant influence on the duration of a project phase. In the paper, some methodologies of the knowledge discovery process are compared and a model of knowledge discovery from an ERP database is proposed. The presented approach is dedicated for the industrial enterprises that use an ERP system to plan and control the development of new products. The example contains four stages of the knowledge discovery process, such as data selection, data transformation, data mining, and the interpretation of patterns. Among data mining techniques, a fuzzy neural system is chosen to seek relationships between data from completed projects and other data stored in an ERP system.
Źródło:
Informatyka Ekonomiczna; 2013, 2(28); 100-111
1507-3858
Pojawia się w:
Informatyka Ekonomiczna
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Outlier detection in ocean wave measurements by using unsupervised data mining methods
Autorzy:
Mahmoodi, K.
Ghassemi, H.
Powiązania:
https://bibliotekanauki.pl/articles/260330.pdf
Data publikacji:
2018
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
ocean wave data
data mining
outlier detection
data correction
Opis:
Outliers are considerably inconsistent and exceptional objects in the data set that do not adapt to expected normal condition. An outlier in wave measurements may be due to experimental and configuration errors, technical defects in equipment, variability in the measurement conditions, rare or unknown conditions such as tsunami, windstorm and etc. To improve the accuracy and reliability of an built ocean wave model, or to extract important and valuable information from collected wave data, detecting of outlying observations in wave measurements is very important. In this study, three typical outlier detection algorithms:Box-plot (BP), Local Distance-based Outlier Factor (LDOF), and Local Outlier Factor (LOF) methods are used to detect outliers in significant wave height (Hs) records. The historical wave data are taken from National Data Buoy Center (NDBC). Finally, those data points are considered as outlier identified by at least two methods which are presented and discussed. Then, Hs prediction has been modelled with and without the presence of outliers by using Regression trees (RTs).
Źródło:
Polish Maritime Research; 2018, 1; 44-50
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance test on triple heap sort algorithm
Autorzy:
Marszałek, Z.
Powiązania:
https://bibliotekanauki.pl/articles/298437.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Warmińsko-Mazurski w Olsztynie
Tematy:
computer algorithm
data sorting
data mining
computer analysis
Opis:
Rapid information search in large data sets is one of the most important issues. Quite often it leads sorting strings stored in different cultures, languages. In this work the author presents a modified triple heap algorithm to sort strings for large data sets. Triple heap algorithm is the subject of research and demonstrating its usefulness in applications.
Źródło:
Technical Sciences / University of Warmia and Mazury in Olsztyn; 2017, 20(1); 49-61
1505-4675
2083-4527
Pojawia się w:
Technical Sciences / University of Warmia and Mazury in Olsztyn
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 196

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