Wyniki wyszukiwania - BazTech

Ograniczanie wyników

1 Journal of Medical Informatics & Technologies

1 2004

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 1

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: nadzór częściowy

Sortuj według:

Ogranicz wyniki do:

Knowledge-based clustering as a conceptual and algorithmic environment of biomedical data analysis

Pedrycz W., Gacek A.

Journal of Medical Informatics & Technologies

2004

Vol. 7

KB13--22

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.