Incremental Clustering Based on AutoEncoder

YUAN Xu; YANG Zhen-nan; ZHAO Liang; CHEN Zhi-kui

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Incremental Clustering Based on AutoEncoder

更新时间：2026-09-04

- Incremental Clustering Based on AutoEncoder
- Vol. 33, Issue 3, Pages: 121-125(2016)
- 作者机构：
  
  大连理工大学软件学院,辽宁,大连,116620
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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原旭, 杨镇楠, 赵亮, 陈志奎. 基于AutoEncoder的增量式聚类算法[J]. 微电子学与计算机, 2016, 33(3): 121-125.

YUAN Xu, YANG Zhen-nan, ZHAO Liang, et al. Incremental Clustering Based on AutoEncoder[J]. 2016, 33(3): 121-125.
原旭, 杨镇楠, 赵亮, 陈志奎. 基于AutoEncoder的增量式聚类算法[J]. 微电子学与计算机, 2016, 33(3): 121-125. DOI：

YUAN Xu, YANG Zhen-nan, ZHAO Liang, et al. Incremental Clustering Based on AutoEncoder[J]. 2016, 33(3): 121-125. DOI：

摘要

针对目前数据量增长迅速

数据特征多

存储空间不足等问题

提出了基于AutoEncoder的增量式聚类算法(ANIC).首先利用AutoEncoder学习数据样本的特征

进行低维特征整合

得到数据样本的压缩表示形式

然后在原有聚类结果的基础上

通过一遍式读取数据和动态更新聚类中心点

对新生成样本进行增量式聚类.在对UCI的四个数据集进行聚类时

实验结果表明该算法能够得到优于k均值算法(Kmeans)和模糊c均值算法(FCM)的聚类效果.同时

该算法的时间消耗低

能够识别离群点

能够有效地对不断增加的数据集进行增量式聚类.

Abstract

The rapid growth of data result in a lot of problems such as the data have too many features

the lack of storage space etc. This paper propose a new incremental clustering algorithm based on AutoEncoder. Firstly

the AutoEncoder are used to learn the features of the data

integrate the low-dimensional feature

and get the reduced representation from the raw data. Then run incremental clustering on the new data base on the original clustering results by reading the data once and dynamically update clustering centers. Experimental results show that the proposed algorithm can obtain a comparable clustering performance with k-means algorithm (Kmeans) and fuzzy c-means algorithm (FCM) on the four data set in UCI database. Meanwhile

the time consumption of the proposed algorithm is low

it can achieve incremental clustering and identify the outliers for the increasing data set effectively.

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references

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吴佳, 罗可. 改进的模糊C均值的增量聚类算法[J]. 计算机工程与应用, 2011, 47(23): 141-143. DOI: 141

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