V3COCA: An effective clustering algorithm for complicated objects and its application in breast cancer research and diagnosis

In breast cancer studies, researchers often use clustering algorithms to investigate similarity/dissimilarity among different cancer cases. The clustering algorithm design becomes a key factor to provide intrinsic disease information. However, the traditional algorithms do not meet the latest multiple requirements simultaneously for breast cancer objects. The Variable parameters, Variable densities, Variable weights, and Complicated Objects Clustering Algorithm (V3COCA) presented in this paper can handle these problems very well. The V3COCA (1) enables alternative inputs of none or a series of objects for disease research and computer aided diagnosis; (2) proposes an automatic parameter calculation strategy to create clusters with different densities; (3) enables noises recognition, and generates arbitrary shaped clusters; and (4) defines a flexibly weighted distance for measuring the dissimilarity between two complicated medical objects, which emphasizes certain medically concerned issues in the objects. The experimental results with 10,000 patient cases from SEER database show that V3COCA can not only meet the various requirements of complicated objects clustering, but also be as efficient as the traditional clustering algorithms.