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Role of Data Mining Techniques in Building Smarter and Greener Environment - A Study

机译:数据挖掘技术在建设智能和更环保环境中的作用 - 一项研究

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People invest a larger part of their energy in their home or work environment and for some, these spots are our asylums. As society and innovation progress there is a developing interest for enhancing the knowledge of the situations in which we live and work. Data mining techniques plays a pivotal role in establishing the smarter home and city to greener environment in the past decade. For a smarter environment, sensors can be embedded anywhere while the occupants play out their day by day schedule. The data are collected from the sensor, stored in a database or a network and data mining algorithms can be used to extract interesting patterns. Some of the application like activity recognition such as cooking, watching TV, sleeping etc., For a greener environment, data mining plays a vital role in predicting the energy consumption like mobile phone energy consumption, waste water treatment, building energy use, predicting traffic density, anomaly detection in roads etc. Data mining techniques works efficiently in imbalanced and microarray datasets which bring some disadvantages like over-fitting, poor performance and low efficiency. This paper reviews various data mining approaches to provide better understanding about the different methods that may help interested researchers to work future in establishing the smart and greener environment.
机译:人们在他们的家庭或工作环境中投入了大部分能量,并为某些人提供了这些景点是我们的庇护。随着社会和创新进展,有一种发展利益,可以增强对我们生活和工作的情况的了解。在过去十年中,数据挖掘技术在建立更聪明的家园和绿色环境方面发挥了关键作用。对于更智能的环境,传感器可以嵌入任何地方,而占用者在日期日程日期间发挥作用。从传感器收集数据,存储在数据库中,或者网络和数据挖掘算法可用于提取有趣的模式。一些应用程序,如活动识别,如烹饪,看电视,睡眠等,为更环保的环境,数据挖掘在预测手机能源消耗,废水处理,建筑能源使用,建筑能源使用等能源消耗方面发挥着至关重要的作用密度,道路异常检测等。数据挖掘技术在不平衡和微阵列数据集中有效地工作,带来一些缺点,如过拟合,性能差和低效率。本文审查了各种数据挖掘方法,以便更好地了解不同方法,这些方法可以帮助感兴趣的研究人员在建立智能和更环保的环境方面工作。

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