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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.
The check results are presented as a similarity report, where each of the similarities that have been found has a link to the source. These reports can be downloaded as PDF documents. Unicheck can be used as a stand-alone online tool, or integrated into an LMS (Learning Management System) via plugin, LTI, API or LTI+API types of integrations.
Systems for text similarity detection implement one of two generic detection approaches, one being external, the other being intrinsic. [5] External detection systems compare a suspicious document with a reference collection, which is a set of documents assumed to be genuine. [6]
Free, GPL2 ALLALIGN For DNA, RNA and protein molecules up to 32MB, aligns all sequences of size K or greater, MSA or within a single molecule. Similar alignments are grouped together for analysis. Automatic repetitive sequence filter. Both Local E. Wachtel 2017 Free AMAP: Sequence annealing: Both: Global: A. Schwartz and L. Pachter: 2006: BAli-Phy
Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content [citation needed] as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of ...
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).