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Research of Conventional Data Mining Tools for Big Data Handling in Finance Institutions

机译：金融机构大数据处理中常规数据挖掘工具的研究

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The article investigates the usability of conventional data mining tools for handling data sets generated in financial institutions. It discloses the characteristics of "big data" which reveal limitations and new requirements for analytical software to deal with huge data flows related to financial transactions. The performance characteristics of four different conventional data mining tools, their visualization and clustering models are tested for experimental set of big data extracted from bank local data warehouse. The ranking of critical characteristics is provided for each stage of analysis of big data set.

机译：本文研究了常规数据挖掘工具用于处理金融机构生成的数据集的可用性。它揭示了“大数据”的特征，揭示了分析软件处理与金融交易有关的大数据流的局限性和新要求。针对从银行本地数据仓库提取的实验性大数据集，测试了四种不同的常规数据挖掘工具，其可视化和聚类模型的性能特征。为大数据集的每个分析阶段提供了关键特征的排名。

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来源
《Business information systems workshops》|2013年|35-46|共12页
会议地点 Poznan(PL)
作者
Darius Tamasauskas; Marius Liutvinavicius; Virgilijus Sakalauskas; Dalia Kriksciuniene;
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作者单位

Vilnius University, Lithuania;

Vilnius University, Lithuania;

Vilnius University, Lithuania;

Vilnius University, Lithuania;

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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
big data; data mining tools; banking data; risk assessment;

机译：大数据;数据挖掘工具；银行数据；风险评估;

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Research of Conventional Data Mining Tools for Big Data Handling in Finance Institutions

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