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Wyszukujesz frazę "pattern clustering" wg kryterium: Temat


Wyświetlanie 1-7 z 7
Tytuł:
Accuracy of generalized context patterns in the context based sequential patterns mining
Autorzy:
Ziembiński, R. Z.
Powiązania:
https://bibliotekanauki.pl/articles/206061.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
knowledge discovery
context based sequential pattern mining
sequential context pattern clustering
pattern accuracy
Opis:
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.
Źródło:
Control and Cybernetics; 2011, 40, 3; 585-603
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Environment adaptive lighting systems for smart homes
Autorzy:
Catalbas, M. C.
Powiązania:
https://bibliotekanauki.pl/articles/102529.pdf
Data publikacji:
2017
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
lighting control
fuzzy systems
image segmentation
pattern clustering
smart home
Opis:
In this work, an application of adaptive lighting system is proposed for smart homes. In this paper, it is suggested that, an intelligent lighting system with outdoor adaptation can be realized via a real fisheye image. During the implementation of the proposed method, the fuzzy c-means method, which is a commonly used data clustering method, has been used. The input image is divided into three different regions according to its brightness levels. Then, the RGB image is converted to CIE 1931 XYZ color space; and the obtained XYZ values are converted to x and y values. The parameters of x and y values are shown in CIE Chromaticity Diagram for different regions in the sky. Thereafter, the coordinate values are converted to Correlated Color Temperature by using two different formulas. Additionally, the conversion results are examined with respect to actual and estimated CCT values.
Źródło:
Advances in Science and Technology. Research Journal; 2017, 11, 3; 172-178
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Customer’s Purchase Prediction Using Customer Segmentation Approach for Clustering of Categorical Data
Autorzy:
Singh, Juhi
Mittal, Mandeep
Powiązania:
https://bibliotekanauki.pl/articles/1841413.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
categorical data
clustering algorithm
frequent pattern mining
association rules
customer relationship management
Opis:
Traditional clustering algorithms which use distance between a pair of data points to calculate their similarity are not suitable for clustering of boolean and categorical attributes. In this paper, a modified clustering algorithm for categorical attributes is used for segmentation of customers. Each segment is then mined using frequent pattern mining algorithm in order to infer rules that helps in predicting customer’s next purchase. Generally, purchases of items are related to each other, for example, grocery items are frequently purchased together while electronic items are purchased together. Therefore, if the knowledge of purchase dependencies is available, then those items can be grouped together and attractive offers can be made for the customers which, in turn, increase overall profit of the organization. This work focuses on grouping of such items. Various experiments on real time database are implemented to evaluate the performance of proposed approach.
Źródło:
Management and Production Engineering Review; 2021, 12, 2; 57-64
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Identyfikacja wzorców w finansowych szeregach czasowych z wykorzystaniem hierarchicznych metod grupowania na przykładzie kursu BTC/PLN
Pattern recognition in financial time series using hierarchical clustering. The case of BTC/PLN exchange rate prediction
Autorzy:
Kądziołka, K.
Powiązania:
https://bibliotekanauki.pl/articles/91503.pdf
Data publikacji:
2016
Wydawca:
Warszawska Wyższa Szkoła Informatyki
Tematy:
bitcoin
grupowanie szeregów czasowych
rozpoznawanie wzorców
Bitcoin
time series clustering
pattern recognition
Opis:
W artykule przedstawiono zastosowanie metody Warda do identyfikacji wzorców w finansowych szeregach czasowych, na przykładzie kursu waluty kryptograficznej bitcoin. Wykorzystując zidentyfikowane wzorce, generowano prognozy zmian kursu w analizowanym szeregu dla danych zbioru testowego, które nie zostały wykorzystane w procesie identyfikacji wzorców. Przeciętny absolutny oraz maksymalny błąd prognozy na danych zbioru testowego był niewielki, natomiast zgodność kierunku zmian kursu BTC/PLN na zbiorze testowym wynosiła tylko 60%.
The aim of this article was to present the use of Ward’s method to identify patterns in BTC/PLN exchange rate. Identified patterns were used to predict BTC/PLN movement direction. Mean absolute percentage error and maximal percentage error on the test set were small, but the movement direction was correctly predicted only in 60% of cases.
Źródło:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2016, 10, 14; 37-48
1896-396X
2082-8349
Pojawia się w:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Texture and gene expression analysis of the MRI brain in detection of Alzheimer’s disease
Autorzy:
Bustamam, A.
Sarwinda, D.
Ardenaswari, G.
Powiązania:
https://bibliotekanauki.pl/articles/91834.pdf
Data publikacji:
2018
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
Alzheimer’s disease
MRI
Feature Extraction
Bi-Clustering
Local Binary Pattern
LBP
Opis:
Alzheimer’s disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer’s disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer’s disease into three types: Alzheimer’s, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer’s disease. The ALBP method achieved an average value of accuracy of between 75% - 100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer’s disease with total of bi-cluster is 6.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 2; 111-120
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Heat waves in Poland in the period 1951-2015: trends, patterns and driving factors
Autorzy:
Wibig, J.
Powiązania:
https://bibliotekanauki.pl/articles/108510.pdf
Data publikacji:
2018
Wydawca:
Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
Tematy:
fala upałów
Polska
tendencja
intensywność
skupienie
heat wave
Polska
trend
intensity
blocking index
circulation pattern
k-mean clustering
Opis:
Heat waves were analysed on the basis of maximum daily temperature from 24 meteorological stations in Poland. Heat waves are defined as the longest continuous period during which Tmax (daily maximum air temperature) is equal to or higher than 30°C in at least three days, the mean Tmax during the whole heat wave is equal or higher than 30°C and Tmax does not drop below 25°C during the whole period of heat wave duration. Heat waves occur in Poland from April to September with their maximums in July and August. Four-day-long heat waves are most frequent but the longest one lasted 31 days. The most persistent heat waves were in 1994 and 2015. An increasing trend in heat wave frequency and intensity is observed in Poland, however the increase is statistically significant at only about 60% of analysed stations. Four synoptic patterns favouring heat waves have been distinguished. A strong high over the Azores accompanies all of them, as well as slightly higher than normal pressure over Central Europe – this causes calm and sunny weather over Poland. Strong blocking appears over the North Atlantic during heat wave events, proofing that the development of strong heat waves in Poland is related to large scale circulation and that they are not of local origin. The analysis of the impact of soil moisture in months leading up to the development of heat waves should be the next step in analysis.
Źródło:
Meteorology Hydrology and Water Management. Research and Operational Applications; 2018, 6, 1; 37-45
2299-3835
2353-5652
Pojawia się w:
Meteorology Hydrology and Water Management. Research and Operational Applications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combined classifier based on feature space partitioning
Autorzy:
Woźniak, M.
Krawczyk, B.
Powiązania:
https://bibliotekanauki.pl/articles/331294.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
rozpoznawanie wzorców
system klasyfikujący wielokrotny
algorytm grupowania
algorytm selekcji
algorytm ewolucyjny
pattern recognition
combined classifier
multiple classifier system
clustering algorithm
selection algorithm
evolutionary algorithm
Opis:
This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier, which makes a decision based on a weighted combination of the discriminant functions of the individual classifiers selected for the committee. The weights mentioned above are dependent not only on the classifier identifier, but also on the class number. The proposed approach is based on the results of previous works, where it was proven that such a combined classifier method could achieve significantly better results than simple voting systems. The proposed modification was evaluated through computer experiments, carried out on diverse benchmark datasets. The results are very promising in that they show that, for most of the datasets, the proposed method outperforms similar techniques based on the clustering and selection approach.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 855-866
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-7 z 7

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