Comparative Analysis of Clustering Algorithms for Large-Scale Data sets using Self-Organizing Maps
Keywords:
comparative analysis, clustering algorithms, large-scale data sets, self-organizing maps, dimensionality reduction, input data, lower dimensions, clusters, new enrolled students, academic grades, SOM learning algorithm, cluster analysis, distinct groupings, comparable, dissimilar, prototypes, quantify differences, Euclidean distanceAbstract
However, SOM is also known as one of clustering techniques, since dimensionality reductionmay also be seen as reducing (or clustering) input data to lower dimensions (or clusters). This researchaims to group new enrolled students to a high school based on their academic grades using a SOMlearning algorithm. The goal of cluster analysis is to identify distinct groupings within the data. Theobjects that belong to the same group ought to be comparable to one another, while those belonging todifferent groups ought to be as dissimilar to one another as is practicable. When dealing with clusteringdifficulties, one is particularly interested in the characterization of the clusters through the use ofprototypes, which can be objects that are typical, representational, or representative in nature. There area variety of ways to quantify differences between things. In this particular piece of research, theEuclidean distance was utilized as the comparative tool.References
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