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Tytuł:: Practical aspects of computer-based clinical decision support systems
Autorzy:: Ossowski, A.
Powiązania:: https://bibliotekanauki.pl/articles/332898.pdf
Data publikacji:: 2002
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: zasada diagnostyczna
reguła w bazie danych
eksploracja danych
optymalna klasyfikacja
komputerowy system diagnostyczny
diagnostic principle
rule in database
data mining
optimal classification
computer-based diagnostic system
Opis:: The general problem of computer-based clinical decision support systems is considered. A class of rules in medical databases characteristic for therapeutic decisions has been distinguished. The necessity of application of a complete set of data for learning computerised systems of decision support has been pointed out. Rough set approach is applied to the analysis of the problem. An illustrative example of application of the presented results in the treatment of endometrial and breast cancer is given.
Źródło:: Journal of Medical Informatics & Technologies; 2002, 3; MI61-69
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: High Frequency Rule Synthesis in a Large Scale Multiple Database with MapReduce
Autorzy:: Bisoyi, Sudhanshu Shekhar
Mishra, Pragnyaban
Mishra, Saroja Nanda
Powiązania:: https://bibliotekanauki.pl/articles/2055260.pdf
Data publikacji:: 2022
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: multiple database
frequent itemset
association rule
rule synthesis
MapReduce
HDFS
Opis:: Increasing development in information and communication technology leads to the generation of large amount of data from various sources. These collected data from multiple sources grows exponentially and may not be structurally uniform. In general, these are heterogeneous and distributed in multiple databases. Because of large volume, high velocity and variety of data mining knowledge in this environment becomes a big data challenge. Distributed Association Rule Mining(DARM) in these circumstances becomes a tedious task for an effective global Decision Support System(DSS). The DARM algorithms generate a large number of association rules and frequent itemset in the big data environment. In this situation synthesizing highfrequency rules from the big database becomes more challenging. Many algorithms for synthesizing association rule have been proposed in multiple database mining environments. These are facing enormous challenges in terms of high availability, scalability, efficiency, high cost for the storage and processing of large intermediate results and multiple redundant rules. In this paper, we have proposed a model to collect data from multiple sources into a big data storage framework based on HDFS. Secondly, a weighted multi-partitioned method for synthesizing high-frequency rules using MapReduce programming paradigm has been proposed. Experiments have been conducted in a parallel and distributed environment by using commodity hardware. We ensure the efficiency, scalability, high availability and costeffectiveness of our proposed method.
Źródło:: International Journal of Electronics and Telecommunications; 2022, 68, 2; 177--186
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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