Child Immunization Using Data Analysis

机译：使用数据分析的儿童免疫

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Data mining is the process of sorting through enormous records to find anomalies, patterns as well as correlations to predict outcomes and future trends through data analysis. The desired results give way to new analytical insights and discovery. Its establishment comprises three joined experimental disciplines: figures (the numeric study about information relationships), artificial intelligence and machine learning (algorithms that study from information given to make future predictions that impact industry). In simple words, data mining helps to understand large complex data sheets in less time, with few risks and remove redundant data to make use of the relevant.

机译：数据挖掘是通过巨大的记录进行分类，以找到异常，模式以及通过数据分析预测结果和未来趋势的相关性。所需的结果使新的分析见解和发现。它的成立包括三个加入的实验学科：数据（关于信息关系的数字研究），人工智能和机器学习（从给予的信息学习的算法，以创建影响行业的预测）。简单的单词，数据挖掘有助于在更少的时间内了解大型复杂数据表，很少有风险并删除冗余数据以利用相关的数据。

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《International Conference on Inventive Computing and Informatics》|2017年|550-1138p|共7页
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作者
Nikhita Siringi; Shilpi Sharma;
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中图分类 TP3-53;
关键词
Data analysis; Immunization; Data mining; Healthcare;

机译：数据分析;免疫;数据挖掘;医疗保健;

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4. Child immunization using data analysis [C] . Nikhita Siringi, Shilpi Sharma 2017 International Conference on Inventive Computing and Informatics . 2017

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8. Analysis of Child Welfare Services Expenditure Data and Child Day Care Expenditure Data. Phase 2 Report [R] . 1988

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Child Immunization Using Data Analysis

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