More work on K -Means Clustering Algorithm: The Dimensionality Problem

Abstract

The K-means clustering algorithm is an old algorithm that has been intensely researched owing to its simplicity of implementation. However, there have also been criticisms on its performance, in particular, for demanding the value of K a priori. It is evident from previous researches that providing the number of clusters a priori does not in any way assist in the production of good quality clusters. The objective of this paper is to investigate the usefulness of the K-means clustering in the clustering of high and multi-dimensional data by applying it to biological sequence data which is known for high and multi-dimension. The squared-Euclidean distance and the cosine measure are used as the similarity measures. The silhouette validity index is used first to show K-means algorithm"s inefficiency in the clustering of high and multidimensional data irrespective of the distance or similarity measure employed. A further study was to introduce a preprocessor scheme to the K-means algorithm to automatically initialize a suitable value of K prior to the execution of the K-mean algorithm. The dimensionality problem investigated suggests that the use of the preprocessor improves the quality of clusters significantly for the biological data sets considered. Furthermore, it is then shown that the Kmeans algorithm with preprocessor produces good quality, compact and well-separated clusters of the biological data obtained from a high-dimension-to-low-dimension mapping scheme introduced in the paper. General TermsK means, Clustering, Algorithm.

The rcdk and cluster R packages applied to drug candidate selection

“…The number of atoms: It is also possible to calculate the coordinates for all the atoms present in the molecule: coords <-do.call('rbind' , lapply(atoms, get.point2d)) coords R can compute a set of molecular descriptors, grouped into 5 different categories: dc <-get.desc.categories() dc [1] "hybrid" "constitutional" "topological" [4] "electronic" "geometrical" Category 2 (constitutional), important in QSAR, contains 15 descriptors, which are listed below: [14].…”

“…It measures, for each point M i , the mean distance to each cluster, and the mean distance to the other points in its cluster. Silhouette values range between − 1 and 1 [4]. A Silhouette coefficient with a value near +1 indicates that the point is far from its neighbouring cluster and very close to the cluster to which it is assigned.…”

Review of Clustering Techniques for Finding the Similarity in Articles

“…(2012) [23] They represent a method for checking the usefulness of k-means on biological data. They introduce preprocessor schemes which automatically initialize a reasonable value of k to k-mean algorithm.…”

LKM: A LDA-BasedK-Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks

“…false(normalvfalse) The algorithm cannot deal with the high-dimensional data effectively. Literatures [16, 17] failed to solve the fusion problem of K -means and dimension reduction; Napoleon and Pavalakodi proposed PCA-Km algorithm [18], which applied PCA on original data set and obtained a reduced data set containing possibly uncorrelated variables; Ding and He proposed a coherent framework to adaptively select the most discriminative subspace [19].…”