Clustering Algorithms for Biological Data-A Survey Approach

Data mining and knowledge extraction is a significant problem in bioinformatics. Biological data mining is an emerging field of research and development. Essential knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays a very important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Clustering analysis has become an obligatory investigative technique for many problems in bioinformatics. Clustering is the task of grouping a set of objects into different subsets such that objects belonging to the same cluster are highly similar to each other. Several clustering algorithms like K-Means clustering, Fuzzy c means clustering, probabilistic clustering, and so on have been proposed in the literature. One common technique is supervised and unsupervised clustering to partition the experimental data. This paper presents a survey on various clustering techniques that are employed for integrating biological data. This paper further provides an overview of limitations of some of the clustering techniques that are proposed in literature. The future enhancement section of this paper discusses some of the fundamental ideas to improve the clustering accuracy of the earlier proposed algorithms.