Telecom complaint hot topic detection method based on density peaks clustering

Jun JIANG; Hua HUANG; Tiaojuan REN; Denghui ZHANG

doi:10.11959/j.issn.1000-0801.2019076

您当前的位置：

首页 >

文章列表页 >

Telecom complaint hot topic detection method based on density peaks clustering

research and development | 更新时间：2024-06-05

- Telecom complaint hot topic detection method based on density peaks clustering
- Telecommunications Science Vol. 35, Issue 5, Pages: 97-103(2019)
- 作者机构：
  
  1. 浙江树人大学信息科技学院，浙江杭州 310015
  2. 浙江大学信息与电子工程学院，浙江杭州 310058
  3. 东方通信股份有限公司，浙江杭州 310053
  4. 万向集团公司万向研究院，浙江杭州 311215
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2019076
  CLC： TP391
- Published Online：2019-05，
  
  Published：20 May 2019
- 稿件说明：
移动端阅览
Jun JIANG, Hua HUANG, Tiaojuan REN, et al. Telecom complaint hot topic detection method based on density peaks clustering[J]. Telecommunications science, 2019, 35(5): 97-103.
DOI：

Jun JIANG, Hua HUANG, Tiaojuan REN, et al. Telecom complaint hot topic detection method based on density peaks clustering[J]. Telecommunications science, 2019, 35(5): 97-103. DOI： 10.11959/j.issn.1000-0801.2019076.

摘要

针对电信业对投诉热点话题缺乏有效的检测方法问题，提出一种基于峰值密度聚类算法的投诉热点话题检测方法。首先建立电信业专用词库用于投诉样本的文本分词，采用向量空间模型表示文本分词，然后通过计算文本分词相似度和密度，并运用密度峰值聚类算法对分词进行聚类分析。最终通过类簇关键词选取并排序，从而得到热点话题描述。将本方法应用到某电信企业投诉热点话题检测中，结果表明本方法有效并具有实际应用价值。

Abstract

In view of the lack of effective detection methods for hot topics in telecom industry

a method of complaint hotspots detection based on density peaks clustering algorithm was proposed.Firstly

a special vocabulary for telecommunication industry was established to segment the complaint samples.The vector space model was used to represent the text segmentation.Then

the similarity and density of the text segmentation were calculated

and the clustering analysis of the words was carried out by using the density peaks clustering algorithm.Finally

keywords were selected and sorted by clustering.This method was applied to the complaint hotspots detection tasks within a telecom company.The results show that this method is effective and has practical application value.

关键词

Keywords

references

LI B L , YU S W . Research on topic detection and tracking [J ] . Computer Engineering and Applications , 2003 , 39 ( 17 ): 7 - 10 .

关晓惠 , 钱亚冠 , 孙欣欣 . 一种改进的基于局部密度的聚类算法 [J ] . 电信科学 , 2016 , 32 ( 1 ): 54 - 59 .

GUAN X H , QIAN Y G , SUN X X . An improved clustering algorithm basing on local density [J ] . Telecommunications Science , 2016 , 32 ( 1 ): 54 - 59 .

周坚 , 石永革 , 何美斌 . 基于A-D模型的K-means算法在通话异常客户挖掘中的应用 [J ] . 电信科学 , 2018 , 34 ( 4 ): 81 - 89 .

ZHOU J , SHI Y G , HE M B , et al . Application of K-means algorithm based on A-D model in calling abnormal customer mining [J ] . Telecommunications Science , 2018 , 34 ( 4 ): 81 - 89 .

MOHD M , CRESTANI F , RUTHVEN I . Construction of topics and clusters in topic detection and tracking tasks [C ] // International Conference on Semantic Technology and Information Retrieval,June 28-29,2011,Putrajaya,Malaysia . Piscataway:IEEE Press , 2011 : 171 - 174 .

ZHANG X M , LI Z J , CHAO W H . Research of automatic topic detection based on incremental clustering [J ] . Journal of Software , 2012 , 23 ( 6 ): 1578 - 1587 .

WANG W , YANG W , QI H F . Netword hotspt topic detection algorithm based on multi-center model [J ] . Journal of Nanjing University of Science and Technology , 2009 , 33 ( 4 ): 422 - 421 .

CHENG W , LONG Z Y . Oline topic detection algorithm for internet news [J ] . Computer Engineering , 2009 , 35 ( 18 ): 28 - 38 .

LI C , SUN A , DATTA A . Twevent:segment-based event detection from tweets [C ] // International Conference on Information and Knowledge Management,Oct 29-Nov 2,2012,San Francisco,USA . Piscataway:IEEE Press , 2012 : 155 - 164 .

VISWANATH P , SURESH B V . Rough-DBSCAN:a fast hybrid density based clustering method for large data sets [J ] . Pattern Recognition Letters , 2009 , 30 ( 16 ): 1477 - 1488 .

KUMAR K M , RAMA M R A . A fast DBSCAN clustering algorithm by accelerating neighbor searching using groups method [J ] . Pattern Recognition , 2016 ( 58 ): 39 - 48 .

谢静瑶 , 解思江 , 焦阳 , 等 . 一种改进的启发式自适应DBSCAN 聚类算法的研究及其在电力系统信息安全预警分析中的应用 [J ] . 电信科学 , 2017 , 33 ( Z1 ): 117 - 122 .

XIE J Y , XIE S J , JIAO Y , et al . Research on an improved heuristic auto-adapted DBSCAN clustering algorithm and the application in power system information security early-warning analysis [J ] . Telecommunications Science , 2017 , 33 ( Z1 ): 117 - 122 .

SUN M X , LIU C Q . Research on hot topic detection based on DBSCAN algorithm and inter sentence relationship [J ] . Library＆ Information Service , 2017 ( 12 ): 113 - 121 .

RODRIGUEZ A , LAIO A . Clustering by fast search and find of density peaks [J ] . Science , 2014 , 344 ( 6191 ): 1492 - 1496 .

WEI M , XU W R , JUN G . A chinese text classifier based on N-gram language model and chain augmented naïve bayesian classifier [J ] . Journal of Chinese Information Processing , 2006 , 20 ( 3 ): 29 - 35 .

WU Y , WEI G , LI H . Word segmentation algorithm for chinese language based on N-gram models and machine learning [J ] . Journal of Electronics and Information Technology , 2001 , 23 ( 11 ): 1148 - 1153 .

TANG G , XIA Y Q , ZHANG M , et al . Cross-lingual document clustering based on similarity space model [J ] . Journal of Chinese Information Processing , 2016 , 26 ( 2 ): 116 - 120 .

WU C Y . Model of Chinese words rough segmentation based on bi-gram and N-most-probability method [J ] . Journal of Computer Applications , 2007 , 27 ( 2 ): 2902 - 2905 .

MA C L , SHAN H , MA T . Improved density peaks based clustering algorithm with strategy choosing cluster center automatically [J ] . Computer Science , 2016 , 43 ( 7 ): 255 - 280 .

CAO Z W , ZHOU Y . Design and implementation of JP algorithm based on MapReduce [J ] . Computer Engineering , 2012 , 38 ( 24 ): 14 - 16 .

VATHY-FOGARASSY A , KISS A , ABONYI J . Improvement of Jarvis-Patrick clustering based on fuzzy similarity [C ] // International Workshop on Fuzzy Logic and Applications:Applications of Fuzzy Sets Theory,July 7-10,2007,Camogli,Italy . New York:ACM Press , 2007 : 195 - 202 .

CHENG Y Y , ZHOU P . Improved K-means clustering algorithm for dynamic allocation cluster center [J ] . Computer Technology and Development , 2017 , 27 ( 2 ): 33 - 36 .

Views

263

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A method for detecting abnormal electricity prices at charging stations based on two rounds of clustering

Related Author

XUE Xiaohui

ZHANG Wen

ZHANG Jing

CHEN Yan

ZHOU Chun

CHEN Liang

CAO Zengwei

Related Institution

State Grid Qinghai Electric Power Company

Power Information Technology Co., Ltd.

⁰