Graph partitioning advance clustering technique
This is an incremental method for clustering in data analysis, with no specific problem or audience mentioned.
The paper tackles clustering by applying graph partitioning techniques, specifically using Fiedler's approach with the Laplacian eigenvector and K-means to partition m-dimensional lattice graphs, but no concrete results or numbers are provided.
Clustering is a common technique for statistical data analysis, Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. Often, distance measures are used. Clustering is an unsupervised learning technique, where interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. This paper also considers the partitioning of m-dimensional lattice graphs using Fiedler's approach, which requires the determination of the eigenvector belonging to the second smallest Eigenvalue of the Laplacian with K-means partitioning algorithm.