CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
GCA: A Coclustering Algorithm for Thalamo-Cortico-Thalamic Connectivity Analysis
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Coclustering for cross-subject fiber tract analysis through diffusion tensor imaging
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
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The fundamental goal of computational neuroscience is to discover anatomical features that reflect the functional organization of the brain. Investigations of the physical connections between neuronal structures and measurements of brain activity in vivo have given rise to the concepts of anatomical and functional connectivity, which have been useful for our understanding of brain mechanisms and their plasticity. However, at present there is no generally accepted computational framework for the quantitative assessment of cortical connectivity. In this paper, we present accurate analytical and modeling tools that can reveal anatomical connectivity pattern and facilitate the interpretation of high-level knowledge regarding brain functions are strongly demanded. We also present a coclustering algorithm, called Business model based Coclustering Algorithm (BCA), which allows an automated and reproducible assessment of the connectivity pattern between different cortical areas based on Diffusion Tensor Imaging (DTI) data. The proposed BCA algorithm not only partitions the cortical mantel into well-defined clusters, but at the same timemaximizes the connection strength between these clusters. Moreover, the BCA algorithm is computationally robust and allows both outlier detection as well as operator-independent determination of the number of clusters. We applied the BCA algorithm to human DTI datasets and show good performance in detecting anatomical connectivity patterns in the human brain.