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Clustered approach to ICT services utilization analysis
Petr Doucek, Ota Novotný, 2012, izvirni znanstveni članek

Opis: The paper describes clustered approach to ICT services utilization analysis based on the WSA method. It allows extracting coherent groups of countries with nearly the same level of ICT services utilization based on the number of indicators analyzed. Approach is explained on case of the Czech Republic and its position in the European peloton with using available Eurostat data.
Ključne besede: informatization, clustering, WSA method, ICT Services
Objavljeno: 29.11.2017; Ogledov: 514; Prenosov: 242
.pdf Celotno besedilo (1,07 MB)
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Robust clustering of languages across Wikipedia growth
Kristina Ban, Matjaž Perc, Zoran Levnajić, 2017, izvirni znanstveni članek

Opis: Wikipedia is the largest existing knowledge repository that is growing on a genuine crowdsourcing support. While the English Wikipedia is the most extensive and the most researched one with over 5 million articles, comparatively little is known about the behaviour and growth of the remaining 283 smaller Wikipedias, the smallest of which, Afar, has only one article. Here, we use a subset of these data, consisting of 14 962 different articles, each of which exists in 26 different languages, from Arabic to Ukrainian. We study the growth of Wikipedias in these languages over a time span of 15 years. We show that, while an average article follows a random path from one language to another, there exist six well-defined clusters of Wikipedias that share common growth patterns. The make-up of these clusters is remarkably robust against the method used for their determination, as we verify via four different clustering methods. Interestingly, the identified Wikipedia clusters have little correlation with language families and groups. Rather, the growth of Wikipedia across different languages is governed by different factors, ranging from similarities in culture to information literacy.
Ključne besede: Wikipedia, language, growth dynamics, data analysis, clustering
Objavljeno: 13.11.2017; Ogledov: 708; Prenosov: 313
.pdf Celotno besedilo (1004,06 KB)
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Innovative solution for energy efficient urban freight deliveries
Tomislav Letnik, Matej Mencinger, Stanislav Božičnik, 2017, objavljeni znanstveni prispevek na konferenci

Ključne besede: transport, urban freight, CO2 emissions, energy efficiency, fuzzy clustering
Objavljeno: 27.09.2017; Ogledov: 630; Prenosov: 71
.pdf Celotno besedilo (14,12 MB)
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Slovenian Entrepreneurship Observatory 2003
Miroslav Rebernik, Dijana Močnik, Jožica Knez-Riedl, Polona Tominc, Karin Širec, Matej Rus, Tadej Krošlin, Silvo Dajčman, končno poročilo o rezultatih raziskav

Opis: The monograph Slovenian Entrepreneurship Observatory 2003 consists of several research issues. In the first part, a short review of the current level of entrepreneurship is given, outlined on the basis of economic and statistical data. Understanding what is happening in Slovenian enterprises is important not only in order to pursue an appropriate economic policy but also in order to find the advantages and disadvantages of Slovenian companies in comparison with enterprises in other European countries. If Slovenia wishes to join the most developed European countries, it will have to speed up its economic growth. In the second part of a monograph, a number of topics, based on a survey of a sample of 672 enterprises are dealt with. We studied the relationship between banks and small and medium-sized enterprises (SMEs), female entrepreneurship, clustering, social responsibility of companies and the development of competences.
Ključne besede: Companies Demography, Financing SMEs, Female Entrepreneurship, Clustering, Environmental Responsibility
Objavljeno: 18.01.2017; Ogledov: 1038; Prenosov: 341
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An efficient k'-means clustering algorithm
Krista Rizman Žalik, 2008, izvirni znanstveni članek

Opis: This paper introduces k'-means algorithm that performs correct clustering without pre-assigning the exact number of clusters. This is achieved by minimizing a suggested cost-function. The cost-function extends the mean-square-error cost-function of k-means. The algorithm consists of two separate steps. The first is a pre-processing procedure that performs initial clustering and assigns at least one seed point to each cluster. During the second step, the seed-points are adjusted to minimize the cost-function. The algorithm automatically penalizes any possible winning chances for all rival seed-points in subsequent iterations. When the cost-function reaches a global minimum, the correct number of clusters is determined and the remaining seed points are located near the centres of actual clusters. The simulated experiments described in this paper confirm good performance of the proposed algorithm.
Ključne besede: algorithms, clustering analysis, k-means, cost-function, rival penalized mechanism, datasets
Objavljeno: 31.05.2012; Ogledov: 1637; Prenosov: 95
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