Temat: data uncertainty - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Designing medical production rules from semantically integrated data
Autorzy:: Jankowska, B.
Szymkowiak, M.
Powiązania:: https://bibliotekanauki.pl/articles/333095.pdf
Data publikacji:: 2010
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: integracja danych semantycznych
medyczne systemy oparte na regułach
niepewność
semantic data integration
medical rule-based systems
uncertainty
Opis:: In the paper an algorithm for automatic knowledge acquisition is proposed. The knowledge is acquired from aggregate data stored in different repositories. The algorithm operates by means of semantic data integration, allowing both syntax and semantic differences between data coming from different sources. If only we know data taxonomies, can interpret data schemas and design schema mappings, then the differences are not an obstacle to integration. The acquired knowledge is being defined in a form of production rules with uncertainty. The considerations are illustrated with medical examples.
Źródło:: Journal of Medical Informatics & Technologies; 2010, 16; 95-102
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Knowledge-based clustering as a conceptual and algorithmic environment of biomedical data analysis
Autorzy:: Pedrycz, W.
Gacek, A.
Powiązania:: https://bibliotekanauki.pl/articles/333706.pdf
Data publikacji:: 2004
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: wiedza i dane
grupowanie rozmyte
bliskość
włączenie
nadzór częściowy
niepewność
entropia
knowledge and data
fuzzy clustering
guidance mechanisms
proximity
inclusion
partial supervision
uncertainty
entropy
Opis:: While a genuine abundance of biomedical data available nowadays becomes a genuine blessing, it also posses a lot of challenges. The two fundamental and commonly occurring directions in data analysis deal with its supervised or unsupervised pursuits. Our conjecture is that in the area of biomedical data processing and understanding where we encounter a genuine diversity of patterns, problem descriptions and design objectives, this type of dichotomy is neither ideal nor the most productive. In particular, the limitations of such taxonomy become profoundly evident in the context of unsupervised learning. Clustering (being usually regarded as a synonym of unsupervised data analysis) is aimed at determining a structure in a data set by optimizing a given partition criterion. In this sense, a structure emerges (becomes formed) without a direct intervention of the user. While the underlying concept looks appealing, there are numerous sources of domain knowledge that could be effectively incorporated into clustering mechanisms and subsequently help navigate throughout large data spaces. In unsupervised learning, this unified treatment of data and domain knowledge leads to the general concept of what could be coined as knowledge-based clustering. In this study, we discuss the underlying principles of this paradigm and present its various methodological and algorithmic facets. In particular, we elaborate on the main issues of incorporating domain knowledge into the clustering environment such as (a) partial labelling, (b) referential labelling (including proximity and entropy constraints), (c) usage of conditional (navigational) variables, (d) exploitation of external structure. Presented are also concepts of stepwise clustering in which the structure of data is revealed via a series of refinements of existing domain granular information.
Źródło:: Journal of Medical Informatics & Technologies; 2004, 7; KB13-22
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Reliability of medical production rules obtained by means of aggregate data mining
Autorzy:: Szymkowiak, M.
Jankowska, B.
Powiązania:: https://bibliotekanauki.pl/articles/333089.pdf
Data publikacji:: 2010
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: całokształt eksploracji danych medycznych
zasada produkcji z niepewnością
niezawodność globalnej i wewnętrzne reguły
medical aggregate data mining
production rule with uncertainty
internal and global rule's reliability
Opis:: In the paper a method for designing production rules with uncertainty from medical aggregate data is proposed. Our main goal is to define the parameters that have an influence on the reliability of obtained rules. We distinguish two factors of reliability: global and internal ones. They determine a rule's importance in comparison to other obtained rules. Those rules compose the knowledge base of a medical Rule-Based System (RBS) aiding medical diagnosis and treatment.
Źródło:: Journal of Medical Informatics & Technologies; 2010, 16; 103-110
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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