大脑网络分析:数据挖掘的视角

Xiangnan Kong, Philip S. Yu
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引用次数: 45

摘要

随着近年来神经成像技术的进步,脑网络分析研究成为数据挖掘界的一个新兴领域。脑网络数据对数据挖掘研究提出了许多独特的挑战。例如,在大脑网络中,节点(即大脑区域)和边缘(即大脑区域之间的关系)通常不给定,而应该从神经成像数据中推导出来。网络结构可能非常嘈杂和不确定。因此,脑网络分析需要创新的方法。许多研究工作都致力于这个领域。它们在脑网络提取、图挖掘、神经成像数据分析等各种应用中取得了巨大的成功。本文综述了近年来文献中用于脑网络数据挖掘的几种数据挖掘方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain network analysis: a data mining perspective
Following the recent advances in neuroimaging technology, the research on brain network analysis becomes an emerging area in data mining community. Brain network data pose many unique challenges for data mining research. For example, in brain networks, the nodes (i.e., the brain regions) and edges (i.e., relationships between brain regions) are usually not given, but should be derived from the neuroimaging data. The network structure can be very noisy and uncertain. Therefore, innovative methods are required for brain network analysis. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as brain network extraction, graph mining, neuroimaging data analysis. In this paper, we review some recent data mining methods which are used in the literature for mining brain network data.
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