基于多尺度聚类的在线学习数据深度挖掘模型研究

Lijuan Liu
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引用次数: 0

摘要

在线学习数据挖掘模型没有考虑数据的非线性,存在查全率、查准率和MMR低的问题。采用支持向量机对在线学习数据进行分类,并采用层次插值方法对在线学习数据中的噪声进行抑制。本文采用多尺度聚类算法对数据关联规则进行递归处理,通过回归分类树构建在线学习数据深度挖掘模型,并借助相似递归函数求解深度挖掘模型的最优解,完成在线学习数据深度挖掘。结果表明,该方法与理想的分层结果分布具有较高的拟合度,数据挖掘的精密度、召回率和MRR均优于传统方法。
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Research on deep mining model of online learning data based on multiscale clustering
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来源期刊
CiteScore
0.70
自引率
0.00%
发文量
95
期刊介绍: IJICT is a refereed journal in the field of information and communication technology (ICT), providing an international forum for professionals, engineers and researchers. IJICT reports the new paradigms in this emerging field of technology and envisions the future developments in the frontier areas. The journal addresses issues for the vertical and horizontal applications in this area. Topics covered include: -Information theory/coding- Information/IT/network security, standards, applications- Internet/web based systems/products- Data mining/warehousing- Network planning, design, administration- Sensor/ad hoc networks- Human-computer intelligent interaction, AI- Computational linguistics, digital speech- Distributed/cooperative media- Interactive communication media/content- Social interaction, mobile communications- Signal representation/processing, image processing- Virtual reality, cyber law, e-governance- Microprocessor interfacing, hardware design- Control of industrial processes, ERP/CRM/SCM
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