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In a relative less span of time we can process and store a large quantity of data due to technological advancements. There is a rapid change in the nature of data, specifically, the dimensional property of data, mostly in multi and high-dimensional. In terms of heterogeneity of data, Data analysis have becoming a humungous task, Because the volume and complexity in data has been increasing incrementally. In data mining, there is a tool called Data clustering, used in many disciplines in order to extract the meaningful knowledge from seemingly unstructured data. The high-dimensional patient's health records such as immune system status, DICOM Images like CT/PET images, electronic medical records, microarray data like gene expressions, genetic background, etc., In this article we have done a survey on high dimensional medical data clustering and different approaches related to this problem. It also focusses on the real-life applications and recent methods in high dimensional cluster analysis. |
