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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.
Similarity measures play a crucial role in many clustering techniques, as they are used to determine how closely related two data points are and whether they should be grouped together in the same cluster. A similarity measure can take many different forms depending on the type of data being clustered and the specific problem being solved.
The test statistic R is calculated in the following way: R = r B − r W M / 2 {\displaystyle R={\frac {r_{B}-r_{W}}{M/2}}} where r B is the average of rank similarities of pairs of samples (or replicates) originating from different sites, r W is the average of rank similarity of pairs among replicates within sites, and M = n ( n − 1)/2 where ...
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).
Statistical inference can be made based on the Jaccard similarity index, and consequently related metrics. [6] Given two sample sets A and B with n attributes, a statistical test can be conducted to see if an overlap is statistically significant. The exact solution is available, although computation can be costly as n increases. [6]
Wes McKinney is an American software developer and businessman. He is the creator and "Benevolent Dictator for Life" (BDFL) of the open-source pandas package for data analysis in the Python programming language, and has also authored three versions of the reference book Python for Data Analysis.
In statistics, the Bhattacharyya distance is a quantity which represents a notion of similarity between two probability distributions. [1] It is closely related to the Bhattacharyya coefficient, which is a measure of the amount of overlap between two statistical samples or populations.
The SMC is very similar to the more popular Jaccard index. The main difference is that the SMC has the term in its numerator and denominator, whereas the Jaccard index does not. Thus, the SMC counts both mutual presences (when an attribute is present in both sets) and mutual absence (when an attribute is absent in both sets) as matches and ...