An investigation of K‐means clustering to high and multi‐dimensional biological data

Abstract

Purpose

The K‐means clustering algorithm has been intensely researched owing to its simplicity of implementation and usefulness in the clustering task. However, there have also been criticisms on its performance, in particular, for demanding the value of K before the actual clustering task. 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 authors' investigations in this paper also confirm this finding. The purpose of this paper is to investigate further, the usefulness of the K‐means clustering in the clustering of high and multi‐dimensional data by applying it to biological sequence data.

Design/methodology/approach

The authors suggest a scheme which maps the high dimensional data into low dimensions, then show that the K‐means algorithm with pre‐processor produces good quality, compact and well‐separated clusters of the biological data mapped in low dimensions. For the purpose of clustering, a character‐to‐numeric conversion was conducted to transform the nucleic/amino acids symbols to numeric values.

Findings

A preprocessing technique has been suggested.

Originality/value

Conceptually this is a new paper with new results.

Citation

Baridam, B.B. and Montaz Ali, M. (2013), "An investigation of K‐means clustering to high and multi‐dimensional biological data", Kybernetes, Vol. 42 No. 4, pp. 614-627. https://doi.org/10.1108/K-02-2013-0028