• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Inventive Computation Technologies >Empirical analysis and improvement of density based clustering algorithm in data streams
【24h】

Empirical analysis and improvement of density based clustering algorithm in data streams

机译：基于密度的数据流聚类算法的实证分析与改进

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Data mining has gained much importance in the field of research these days. It makes perfect blend for analyzing data of any fields and provide decision based output. Data generation and storage these days are done at high speed. Non stationary systems play holistic role in providing such data. Availability of such data creates scope of analysis for researchers. Such data which are continuous, unbounded, fast are termed as stream data. Clustering is the best method for analysis of stream data. As labeling of data is not possible for streams so clustering may assist this process. Also framing clusters digs out the points which do not seem to be part of cluster thus assisting outlier detection also. Different kinds of clustering algorithm exists but density based method helps in detecting clusters of arbitrary shape which other algorithm does not do. We have discussed an approach and presented its result in comparison of existing algorithm in this paper.
机译：数据挖掘目前在研究领域获得了很多。它是完美的混合，用于分析任何字段的数据并提供基于判定的输出。这些天数据生成和存储是高速完成的。非静止系统在提供此类数据方面发挥全部作用。此类数据的可用性为研究人员创造了分析范围。这种数据是连续的，无界，快速被称为流数据。聚类是用于分析流数据的最佳方法。由于流的标签是不可能进行流的，因此群集可以帮助此过程。框架簇也挖掘似乎并不是集群的一部分的点，从而辅助异常检测。存在不同种类的聚类算法，但基于密度的方法有助于检测其他算法不做的任意形状的集群。我们已经讨论了一种方法，并呈现了本文现有算法的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号