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A similarity measure can take many different forms depending on the type of data being clustered and the specific problem being solved. One of the most commonly used similarity measures is the Euclidean distance, which is used in many clustering techniques including K-means clustering and Hierarchical clustering. The Euclidean distance is a ...
An analysis by Schwarzer et al. [4] showed that the citation-based measures CPA and co-citation analysis, have complementary strengths compared to text-based similarity measures. Text-based similarity approaches reliably identified more narrowly similar articles out of a test collection of Wikipedia articles, e.g. articles sharing identical ...
Download as PDF; Printable version; In other projects Wikidata item; ... Pages in category "Similarity measures" The following 10 pages are in this category, out of ...
Bibliographic coupling, like co-citation, is a similarity measure that uses citation analysis to establish a similarity relationship between documents. Bibliographic coupling occurs when two works reference a common third work in their bibliographies. It is an indication that a probability exists that the two works treat a related subject matter.
Testing various clustering algorithms and analyzing their results to find a suitable match for our task (determining which modules are similar and possible candidates to be merged). Also contains a brief literature review of code similarity detection. List of possible candidates for improvement of clustering using better algorithms.
Similarity learning is closely related to distance metric learning.Metric learning is the task of learning a distance function over objects. A metric or distance function has to obey four axioms: non-negativity, identity of indiscernibles, symmetry and subadditivity (or the triangle inequality).
One obvious example is the "find-similar-document" query, on traditional text corpora or the World-Wide Web. More generally, a similarity measure can be used to cluster objects, such as for collaborative filtering in a recommender system, in which “similar” users and items are grouped based on the users’ preferences.
Documents featuring high numbers of co-citations are regarded as more similar. [1] The figure's right image shows a citing document which cites the Documents 1, 2 and 3. Both the Documents 1 and 2 and the Documents 2 and 3 have a co-citation strength of one, given that they are cited together by exactly one other document.