Temat: fuzzy inclusion - Prolib Integro

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Tytuł:: Fuzzy similarity and fuzzy inclusion measures in polyline matching : a case study of potential streams identification for archaeological modelling in GIS
Autorzy:: Ďuračiová, R.
Rášová, A.
Lieskovský, T.
Tematy:: spatial data uncertainty
similarity measure
fuzzy inclusion
spatial object matching
identity determination
niepewność danych przestrzennych
miara podobieństwa
dopasowanie obiektów przestrzennych
określenie tożsamości; Pokaż więcej
Wydawca:: Politechnika Warszawska. Wydział Geodezji i Kartografii
Powiązania:: https://bibliotekanauki.pl/articles/106895.pdf Link otwiera się w nowym oknie
Opis:: When combining spatial data from various sources, it is often important to determine similarity or identity of spatial objects. Besides the differences in geometry, representations of spatial objects are inevitably more or less uncertain. Fuzzy set theory can be used to address both modelling of the spatial objects uncertainty and determining the identity, similarity, and inclusion of two sets as fuzzy identity, fuzzy similarity, and fuzzy inclusion. In this paper, we propose to use fuzzy measures to determine the similarity or identity of two uncertain spatial object representations in geographic information systems. Labelling the spatial objects by the degree of their similarity or inclusion measure makes the process of their identification more efficient. It reduces the need for a manual control. This leads to a more simple process of spatial datasets update from external data sources. We use this approach to get an accurate and correct representation of historical streams, which is derived from contemporary digital elevation model, i.e. we identify the segments that are similar to the streams depicted on historical maps.
Dostawca treści:: Biblioteka Nauki

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Tytuł:: Knowledge-based clustering as a conceptual and algorithmic environment of biomedical data analysis
Autorzy:: Pedrycz, W.
Gacek, A.
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; Pokaż więcej
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Powiązania:: https://bibliotekanauki.pl/articles/333706.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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