With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses many unique challenges for data mining research. For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity, and nonlinear separability. Furthermore, brain connectivity networks can be constructed from the tensor data, embedding subtle interactions between brain regions. Other clinical measures are usually available reflecting the disease status from different perspectives. It is expected that integrating complementary information in the tensor data and the brain network data, and incorporating other clinical parameters will be potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, and multi-view feature analysis. In this paper, we review some recent data mining methods that are used for analyzing brain disorders.

中文翻译：

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综述用于脑部疾病识别的异构数据挖掘。

随着神经成像技术的飞速发展，脑部疾病识别的研究已成为数据挖掘领域的新兴领域。脑部疾病数据对数据挖掘研究提出了许多独特的挑战。例如，由神经影像实验产生的原始数据以张量表示，具有高维度，结构复杂性和非线性可分离性的典型特征。此外，可以从张量数据构建大脑连接网络，从而在大脑区域之间嵌入微妙的交互作用。通常可以使用其他临床方法从不同角度反映疾病状况。期望在张量数据和大脑网络数据中整合补充信息，并结合其他临床参数对于研究疾病机制和提供治疗干预信息可能具有潜在的变革性。在这一领域已经进行了许多研究工作。他们在各种应用中都取得了巨大的成功，例如基于张量的建模，子图模式挖掘和多视图特征分析。在本文中，我们回顾了一些用于分析脑部疾病的最新数据挖掘方法。