Business Intelligence using Machine Learning and Data Mining techniques - An analysis

机译：使用机器学习和数据挖掘技术的商业智能-分析

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Today's social informatics is facing the challenge of exponentially increasing data present online. However this massive amount of data is available across several heterogeneous platform making it a challenging task especially to comprehend useful information and effectively use it for business intelligence. Achieving Business intelligence through machine learning is one of the significant issues in recent era. Previously, outliers were being considered as noisy data and disregarded leading to loss of relevant information. This paper highlights the major research challenges in this mining sub domain. It provides a comprehensive taxonomy extracted for Business Intelligence methodologies along with current application sectors. Future research directions and suggestions have been pointed to address this anomaly gap to achieve effective business strategies

机译：当今的社会信息学正面临着在线呈指数增长数据的挑战。但是，在多个异构平台上都可以获取大量数据，这使它成为一项艰巨的任务，尤其是要理解有用的信息并将其有效地用于商业智能。通过机器学习实现商业智能是近代的重要问题之一。以前，离群值被视为嘈杂数据，而被忽略导致丢失相关信息。本文重点介绍了该挖掘子领域中的主要研究挑战。它为商业智能方法论以及当前的应用领域提供了全面的分类法。指出了未来的研究方向和建议，以解决这一异常差距，以实现有效的业务策略

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来源
《2018 Second International Conference on Electronics, Communication and Aerospace Technology》|2018年|1473-1478|共6页
会议地点 Coimbatore(IN)
作者
Ruchi Sharma; Pravin Srinath;
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作者单位

Department of Information Technology, NMIMS MPSTME, Mumbai;

Computer Engineering Department, NMIMS MPSTME, Mumbai;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Business intelligence; Machine learning; Conferences; Anomaly detection; Feature extraction;

机译：商业智能;机器学习;会议;异常检测;特征提取;;

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机译：统计和机器学习数据挖掘：用于更好预测性建模和大数据分析的技术

Business Intelligence using Machine Learning and Data Mining techniques - An analysis

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