Abstract
Fuzzy co-clustering extends co-clustering by assigning membership functions to both the objects and the features, and is helpful to improve clustering accurarcy of biomedical data. In this paper, we introduce a new fuzzy co-clustering algorithm based on information bottleneck named ibFCC. The ibFCC formulates an objective function which includes a distance function that employs information bottleneck theory to measure the distance between feature data point and the feature cluster centroid. Many experiments were conducted on five biomedical datasets, and the ibFCC was compared with such prominent fuzzy (co-)clustering algorithms as FCM, FCCM, RFCC and FCCI. Experimental results showed that ibFCC could yield high quality clusters and was better than all these methods in terms of accuracy.
Highlights
Nowadays, the amount of biomedical data grows rapidly, which makes it difficult for medical workers and patients to find the information they need
Examples tell us soft clustering may be more reasonable than hard clustering, because many times we cannot put an object into just one cluster
We firstly compared the performances of Fuzzy c-Means (FCM), FCCM, RFCC, FCCI and ibFCC on the six subsets
Summary
The amount of biomedical data grows rapidly, which makes it difficult for medical workers and patients to find the information they need. The clustering technique can identify the latent structure and knowledge behind large-scale biomedical data, and play an important role in reorganizing biomedical data and helping users find relevant information This technique tries to generate a set of clusters where intra-cluster similarity is maximized and inter-cluster similarity is minimized, and is widely used for such applications as automatic categorization of text, grouping gene expression data, and others [1,2]. In recent years many researchers have studied data mining and presented a number of clustering algorithms [3,4,5,6,7]. These algorithms can be divided into hard and soft clustering algorithms [8]. Examples tell us soft clustering may be more reasonable than hard clustering, because many times we cannot put an object into just one cluster
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