斜面系统优化在数据聚类中的应用

M. Mozaffari, H. Abdy, S. Zahiri
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引用次数: 14

摘要

数据挖掘是一门以适当的时间和成本从庞大的数据库中提取一系列未来和一些有意义的信息的科学。聚类是该领域中比较流行的方法之一。集群的目的是使用数据库并将具有相似特征的项分组在一起。聚类在许多科学和工程问题领域的应用,如模式识别、数据检索、生物信息学、机器学习和互联网,在过去的几十年里有了显著的发展。数据库信息量的快速增长暴露了K-means等传统方法在面对海量数据时的弱点。本文提出了一种新的基于斜面系统优化算法的聚类方法,并在一系列标准数据集上进行了评价。对比研究表明,该聚类算法明显优于其他同类聚类算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of inclined planes system optimization on data clustering
Data-mining is a branch of science which tends to extract a series of futures and some meaningful information from a huge database in proper time and cost. Clustering is one of the popular methods in this field. The purpose of clustering is to use a database and group together its items with similar characteristics. Application of clustering in many fields of science and engineering problems like Pattern recognition, data retrieval, bio-informatics, machine learning and the Internet cause to have significantly developed in the last decades. A rapid growth in the volume of information in databases revealed weakness of traditional methods like K-means in facing with huge data. In this paper a new clustering method based on the Inclined Planes system Optimization algorithm was proposed and evaluate on a series of standard datasets. Comparison study revealed a significant superiority over other similar clustering algorithms.
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