Intuitive Clustering of Biological Data

K-means clustering combines a variety of striking properties because of which it is widely used in applications: training is intuitive and simple, the final classifier represents classes by geometrically meaningful prototypes, and the algorithm is quite powerful compared to more complex alternative clustering algorithms. In this contribution, we focus on extensions which incorporate additional information into the clustering algorithm to achieve a better accuracy: neighborhood cooperation from neural gas, (possibly fuzzy) label information of input data, and general problem-adapted distances instead of the standard Euclidean metric. These extensions can be formulated in a simple general framework by means of a cost function. We demonstrate the ability of these variants on several representative clustering problems from computational biology.