基于密度的多变量数据集聚类研究

Shiwani Ghanghas, Abhishek Bhatnagar
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引用次数: 0

摘要

数据挖掘从大型数据库记录中提取隐藏的预测信息,是一项强大的新技术,具有巨大的潜力,可以帮助公司专注于数据价值仓库中最重要的信息。数据挖掘工具预测未来的趋势和行为,允许企业采取主动,知识驱动的决策。由数据挖掘提供的自动化前瞻性分析转移到由决策支持系统典型的回顾性工具提供的对过去事件的分析之外。数据挖掘工具可以回答通常太耗时而无法解决的业务问题。他们在数据库记录记录中寻找隐藏的模式,找到专家可能错过的预测信息,因为这超出了他们的预期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation into Density Based Clustering of Multivariate Data Sets
Data mining extraction of hidden predictive info from large database records, is a powerful new technology with great potential to help companies focus on most important info within their data value warehouses. Data mining utensils predict future trends &behaviors, permitting businesses to make taking the initiative, knowledge motivated decisions. Automated, prospective analyses offered by data mining transfer outside analyses of past events providing by retrospective utensils typical of decision support systems. data mining utensils may answer business questions that usually were too time consuming to resolve. They scour database record records for hidden patterns, finding predictive info that experts may miss since this lies outside their expectations.
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