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    Wyświetlanie 1-7 z 7
    Tytuł:
    Set representation for rule-generation algorithms
    Autorzy:
    Kharkongor, Carynthia
    Nath, Bhabesh
    Powiązania:
    https://bibliotekanauki.pl/articles/27312912.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    item set
    item set representation
    apriori algorithm
    rule-generation algorithm
    data set
    set representation
    bitmap
    memory
    time
    Opis:
    The task of mining association rules has become one of the most widely used discovery pattern methods in knowledge discovery in databases (KDD). One such task is to represent an item set in the memory. The representation of the item set largely depends on the type of data structure that is used for storing them. Computing the process of mining an association rule impacts the memory and time requirements of the item set. With the constant increase of the dimensionality of data and data sets, mining such a large volume of data sets will be difficult since all of these item sets cannot be placed in the main memory. As the representation of an item set greatly affects the efficiency of the rule-mining association, a compact and compressed representation of the item set is needed. In this paper, a set representation is introduced that is more memory- and cost-efficient. Bitmap representation takes 1 byte for an element, but a set representation uses 1 bit. The set representation is being incorporated in the Apriori algorithm. Set representation is also being tested for different rule-generation algorithms. The complexities of these different rule-generation algorithms that use set representation are being compared in terms of memory and time of execution.
    Źródło:
    Computer Science; 2022, 23 (2); 205--225
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Noninvasive blood glucose level monitoring for predicting insulin infusion rate using multivariate data
    Autorzy:
    Geetha, G.
    Ponsam, J. Godwin
    Nimala, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/38709458.pdf
    Data publikacji:
    2024
    Wydawca:
    Instytut Podstawowych Problemów Techniki PAN
    Tematy:
    CGM
    fog computing
    hypoglycemia
    hyperglycemia
    Apriori algorithm
    obliczenie mgły
    hipoglikemia
    hiperglikemia
    Algorytm Apriori
    Opis:
    Diabetes stands as the most widely recognized acute disease globally, resulting in death when it is not treated in an appropriate manner and time. We have developed a closedloop control system that uses continuous glucose, carbohydrate, and physiological variable data to regulate glucose levels and treat hyperglycemia and hypoglycemia, as well as a hypoglycemia early warning module. Overall, the proposed models are effective at predicting a normal glycemic range from >70 to 180 mg/dl, hypoglycemic values of <70 mg/dl, and hyperglycemic value of 180 mg/dl blood sugar levels. We undertook a seven-day, day-and-night home study with 15 adults. Initially, we started with checking insulin levels after meal consumption, and later, we concentrated on how our system reacted to the physical activity of the patients. Evaluation was conducted based on performance parameters such as precision (0.87), recall (0.87), F-score (0.82), delay (26.5±3), and error size (1.14±2).
    Źródło:
    Computer Assisted Methods in Engineering and Science; 2024, 31, 2; 157-174
    2299-3649
    Pojawia się w:
    Computer Assisted Methods in Engineering and Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Finding frequent items: novel method for improving Apriori algorithm
    Autorzy:
    Karimtabar, Noorollah
    Fard, Mohammad Javad Shayegan
    Powiązania:
    https://bibliotekanauki.pl/articles/27312914.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    apriori algorithm
    frequent itemset
    intelligent method
    Opis:
    In this paper, we use an intelligent method for improving the Apriori algorithm in order to extract frequent itemsets. PAA (the proposed Apriori algorithm) pursues two goals: first, it is not necessary to take only one data item at each step – in fact, all possible combinations of items can be generated at each step; and second, we can scan only some transactions instead of scanning all of the transactions to obtain a frequent itemset. For performance evaluation, we conducted three experiments with the traditional Apriori, BitTableFI, TDM-MFI, and MDC-Apriori algorithms. The results exhibited that the algorithm execution time was significantly reduced due to the significant reduction in the number of transaction scans to obtain the itemset. As in the first experiment, the time that was spent to generate frequent items underwent a reduction of 52% as compared to the algorithm in the first experiment. In the second experiment, the amount of time that was spent was equal to 65%, while in the third experiment, it was equal to 46%.
    Źródło:
    Computer Science; 2022, 23 (2); 161--177
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Applying market basket analysis to official statistical data
    Analiza koszykowa i jej zastosowania w statystyce publicznej
    Autorzy:
    Szymkowiak, Marcin
    Klimanek, Tomasz
    Józefowski, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/424821.pdf
    Data publikacji:
    2018
    Wydawca:
    Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
    Tematy:
    market basket analysis
    Apriori algorithm
    National Census of Population and Housing 2011
    marital status
    arules package
    arulesViz package
    Opis:
    Market basket analysis, which is a method of discovering co-occurrence relationships, is widely used for the purposes of marketing research and e-commerce, mainly by supermarkets and online stores. Moving beyond the traditional notion of a market basket understood as a fixed list of products, the technique can be applied for data mining in other fields of research which do not involve traditional transactions and purchases made by customers. The following article describes theoretical aspects of market basket analysis with an illustrative application based on data from the National Census of Population and Housing 2011 with respect to marital status. This is the first application of market basket analysis to census data to be conducted in Poland, in which attributes of the market basket have been replaced with respondents’ demographic characteristics. This approach makes it possible to identify relationships between legal (de jure) marital status and actual (de facto) marital status, taking into account other basic socio-demographic variables available in large datasets. Using the R software to generate choropleth maps classified by province as a method of visualizing association rules, it was possible to conduct a spatial analysis of the phenomenon of interest.
    Źródło:
    Econometrics. Ekonometria. Advances in Applied Data Analytics; 2018, 22, 1; 38-57
    1507-3866
    Pojawia się w:
    Econometrics. Ekonometria. Advances in Applied Data Analytics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of data mining techniques to find relationships between the dishes offered by a restaurant for the elaboration of combos based on the preferences of the diners
    Autorzy:
    Vazquez, Rosa Maria
    Bonilla, Edmundo
    Sanchez, Eduardo
    Atriano, Oscar
    Berruecos, Cinthya
    Powiązania:
    https://bibliotekanauki.pl/articles/118001.pdf
    Data publikacji:
    2019
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    data mining
    association rules
    apriori algorithm
    combos
    Web Service
    eksploracja danych
    reguły asocjacji
    algorytm a priori
    kombinacje
    Opis:
    Currently, blended food has been a common menu item in fast food restaurants. The sales of the fast-food industry grow thanks to several sales strategies, including the “combos”, so, specialty, regional, family and buffet restaurants are even joining combos’ promotions. This research paper presents the implementation of a system that will serve as support to elaborate combos according to the preferences of the diners using data mining techniques to find relationships between the different dishes that are offered in a restaurant. The software resulting from this research is being used by the mobile application Food Express, with which it communicates through webservices. References
    Źródło:
    Applied Computer Science; 2019, 15, 2; 73-88
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of apriori algorithm in the lamination process in yacht production
    Autorzy:
    Niksa-Rynkiewicz, Tacjana
    Landowski, Michal
    Szalewski, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/1585049.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    apriori algorithm
    quality control
    laminate defects
    Opis:
    The article specifies the dependence of defects occurring in the lamination process in the production of yachts. Despite great knowledge about their genesis, they cannot be completely eliminated. Authentic data obtained through cooperation with one of the Polish yacht shipyards during the years 2013–2017 were used for the analysis. To perform a simulation, the sample size was observed in 1450 samples, consisting of 6 models of yachts with closed and open deck. Finding the dependence of the occurrence of specific defects will allow for faster procedures and more effective quality control, which will contribute to lower costs. The use of new methods based on artificial intelligence related to Big Data allows for easier observation of dependencies in the complex structure of data from yacht production. The association rules were defined using the algorithm Apriori and define interdependent defects. A number of dependencies were found for the occurrence of production defects not obvious to technologists, but occurring with a high probability of coexistence. The presented research results may allow the planning process of production tasks to be improved.
    Źródło:
    Polish Maritime Research; 2020, 3; 59-70
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An Efficient Hybrid Classifier Model for Customer Churn Prediction
    Autorzy:
    Anitha, M. A.
    Sherly, K. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/2200701.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    customer churn prediction
    bag of learners
    ANN
    SVM
    regression
    associative classifier
    Apriori Algorithm
    Opis:
    Customer churn prediction is used to retain customers at the highest risk of churn by proactively engaging with them. Many machine learning-based data mining approaches have been previously used to predict client churn. Although, single model classifiers increase the scattering of prediction with a low model performance which degrades reliability of the model. Hence, Bag of learners based Classification is used in which learners with high performance are selected to estimate wrongly and correctly classified instances thereby increasing the robustness of model performance. Furthermore, loss of interpretability in the model during prediction leads to insufficient prediction accuracy. Hence, an Associative classifier with Apriori Algorithm is introduced as a booster that integrates classification and association rule mining to build a strong classification model in which frequent items are obtained using Apriori Algorithm. Also, accurate prediction is provided by testing wrongly classified instances from the bagging phase using generated rules in an associative classifier. The proposed models are then simulated in Python platform and the results achieved high accuracy, ROC score, precision, specificity, F-measure, and recall.
    Źródło:
    International Journal of Electronics and Telecommunications; 2023, 69, 1; 11--18
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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