Clustering is a popular data mining technique that is used to place data elements into related groups of “similar behaviour”. The traditional clustering algorithm is the socalled kmeans algorithm. However, kmeans has some wellknown problems, i.e. it does not work well on clusters with not welldefined centers, it is difficult to choose the number k of clusters to construct upfront and different initial centers can lead to different final clusters. In recent years, spectral clustering has become popular and widely used since its results often outperform the outcomes of the kmeans algorithm. Spectral clustering is a more advanced algorithm compared to kmeans as it uses several mathematical concepts (i.e. degree matrices, weight matrices, similarity matrices, similarity graphs, graph Laplacians, eigenvalues and eigenvectors) in order to divide similar data points in the same group and dissimilar data points in different groups.
This project's goal was to implement the Spectral Clustering algorithm as a module for the XVDM visual data mining tool. In addition a study of the strengths and weaknesses of the algorithm had to be done by performing experiments using the implemented solution and different, wellchosen, datasets.

OverviewProgramming Language: C++ Architecture: plugin Development State: finished Type: Visual datamining tool  Spectral Clustering
Tags: Spectral clustering, Data mining
