Wyniki wyszukiwania - BazTech

1

The technology of hierarchical agglomerative cluster analysis in library research

Кunenets N.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

|

2016

|

Vol. 5, No 1

35--41

EN

This article describes the simple technology of the hierarchical agglomerative cluster analysis of 20 different libraries, presented by the samples of classification attributes of the same volumes. It is necessary to construct a proximity matrix for an effective process of cluster analysis, using the data from the table “library-classification features”. For separating a set of selected objects into clusters, so that each of them has objects, the most appropriate for its type, it is necessary to create a table “object-property”, where libraries are objects and individual and equal dimension vectors (sets) of characteristic classification features are properties. To do this you should: form the set of libraries that are the objects of clustering; define for each library the set of classification features and its power (volume) in the same nominal scale; choose the value scale of classification features; form a table object-property. This technology is implemented in the environment of MsExcel-2003. It includes the transformation of one-dimensional data into multi-dimensional indexes, using the descriptive statistics and distributions of individual parameters, the creation of the “object-property” table, the building of the proximity matrix, the definition of the dendrogram structure and the cluster interpretation. The application of clustering method can further focus on creating algorithms of effective information search, and also building the scientifically-reasonable classification systems orientated on the library science. The method of hierarchical agglomerative cluster analysis can be used with typological or semantic distribution of library funds, or studying of their thematic and specific composition. This method can be considered as universal, that gives an opportunity to formalize the typology division of any objects of librarianship.

2

Clusterization modules for "Ontology Data Models for Data and Metadata Exchange Repository" system text collections

Shatovska T., Kamenieva I., Liskong O.

Metody Informatyki Stosowanej

|

2009

|

nr 2 (19)

139-144

EN

In this paper the clusterization dataset module for "Ontology Data Models for Data and Metadata Exchange Repository" is considered. This module makes it possible to perform texts clusterization without prior clusters end-points.

3

Wykorzystanie porostów do oceny zanieczyszczenia obszarów leśnych metalami ciężkimi

Kłos A., Rajfur M., Ciesielczuk T., Wacławek M., Wacławek W.

Ecological Chemistry and Engineering. S

|

2008

|

Vol. 15, nr 1

77-84

PL

Prowadzane przez nas badania mają na celu ocenę możliwości wykorzystania porostów jako biomonitorów zanieczyszczenia metalami ciężkimi obszaru Borów Stobrawskich (południowa Polska). Do badań wykorzystano porosty Hypogymnia physodes. W porostach oznaczano stężenia wybranych metali ciężkich: Cu, Ni, Pb i Zn. Wyniki badań zinterpretowano za pomocą dendrogramów, wskazujących na podobieństwa obiektów w wielowymiarowej przestrzeni zmiennych, w tym przypadku na występowanie miejsc o porównywalnej koncentracji badanych analitów: Cu, Ni i Pb. Zanieczyszczenie cynkiem zinterpretowano, uwzględniając źródła jego emisji.

EN

The aim of our investigations was estimation of the lichen Hypogymnia physodes utilization possibility for heavy metals contamination biomonitoring in Bory Stobrawskie (southern Poland). Accumulation of Cu, Ni, Pb and Zn in lichen was studied. The results obtained were interpreted using dendrograms revealing similar objects in multidimensional space of variables, in our work pointing out places with similar analytes concentration: Cu, Ni and Pb. The pollution with zinc was interpreted considering sources of its emission.

4

Zastosowanie hierarchicznych metod grupowania do prognozowania dobowych charakterystyk obciążeń elektroenergetycznych

Dudek G.

Przegląd Elektrotechniczny

|

2006

|

R. 82, nr 9

9-11

PL

Przedstawiono zastosowanie jednej z najbardziej popularnych metod grupowania danych do prognoz dobowych charakterystyk obciążeń elektroenergetycznych. Hierarchiczne metody grupowania tworzą dendrogram, obrazujący klastery profilów prognoz obciążeń dobowych wyznaczone z historii procesu i wzajemne odległości pomiędzy nimi. Klasterom nadaje się etykiety z typami i numerami dni profilów, które formują klaster. Prognozy tworzy się wykorzystując informacje zapisane w etykietach i charakterystykach klasterów. Model przetestowano na rzeczywistych danych.

EN

Application one of the most popular clustering method to the daily electrical load profile forecasting is presented. Hierarchical clustering methods create a dendrogram, which illustrates the daily load profile clusters determined on the process history basis and distances between them. Clusters are being labeled with day types and day numbers of profiles that form a cluster. Information on those labels and cluster profiles serve for creating forecasts. The method was tested on real data.

5

Podziałowe i hierarchiczne algorytmy grupowania danych

Stąpor K.

Studia Informatica

|

2004

|

Vol. 25, nr 1

157-170

PL

W artykule przedstawiono najważniejsze podziałowe oraz hierarchiczne algorytmy grupowania danych. Wśród algorytmów podziałowych omówiono algorytmy oparte na prototypach punktowych oraz liniowych. Przedstawiono algorytmy hierarchiczne dla różnych miar podobieństwa oraz omówiono skrótowo kla-steryzację neuronową wykorzystującą sieć Kohonena.

EN

In this paper the most important partitional and hierarchical data clustering algorithms arę described. Among partitional algorithms those based on point and linear prototypes arę discussed. In hierarchical algorithms different similarity measures arę described. Also, neural network clustering based on Kohonen network is described.

6

On the generalised affinity coefficient for complex data

Bacelar-Nicolau H.

Biocybernetics and Biomedical Engineering

|

2002

|

Vol. 22, no. 1

31-42

EN

This paper concerns the affinity coefficient and extensions for maesuring the similarity between data units in classification, when we are dealing with large and complex databases. More precisely we refer to the extended weighted affinity coefficient and its role in hierarchical classification, when we are dealing with a generalised data table where the cells can contain a set of values, describing a probability distribution, a histogram (frequency distribution), or integer frequencies, for instance, instead one single value. Here we study the case of frequency distributions, since in our approach the other cases appear to be derived as a generalisation (integer frequencies, real data, for instance) or else as a particular case (binary data, ordinal data) of this one. Either the weighted affinity coefficient or the probabilistic associated coefficients can be extended, in the clustering viewpoint, to hierarchical (and non-hierarchical) aggregation criteria and aggregation adaptive (parametric) families. An application to a real case is presented.