Name
Abstract | ||
|---|---|---|
Classically, unsupervised machine learning techniques are applied on data sets with fixed number of attributes ( variables). However, many problems encountered in the field of informetrics face us with the need to extend these kinds of methods in a way such that they may be computed over a set of nonincreasingly ordered vectors of unequal lengths. Thus, in this paper, some new dissimilarity measures (metrics) are introduced and studied. Owing to that we may use, e.g. hierarchical clustering algorithms in order to determine an input data set's partition consisting of sets of producers that are homogeneous not only with respect to the quality of information resources, but also their quantity. (c) 2015 Elsevier Ltd. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.joi.2015.02.005 | Journal of Informetrics |
Keywords | Field | DocType |
Aggregation,Hierarchical clustering,Distance,Metric | Hierarchical clustering,Data mining,Canopy clustering algorithm,Data set,Correlation clustering,Computer science,Informetrics,Unsupervised learning,Artificial intelligence,Cluster analysis,Machine learning,Information quality | Journal |
Volume | Issue | ISSN |
9 | 2 | 1751-1577 |
Citations | PageRank | References |
1 | 0.41 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anna Cena | 1 | 18 | 4.21 |
| Marek Gagolewski | 2 | 186 | 23.86 |
| Radko Mesiar | 3 | 3778 | 472.41 |