Factor analysis on call detail record

Qingli Ma; Wen Wang; Qing Yao; Jingdi Zhou; Lei Quo

doi:10.1109/WOCC.2018.8372724

Factor analysis on call detail record

Qingli Ma, Wen Wang, Qing Yao, Jingdi Zhou, Lei Quo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

We analyze the call detail record (CDR) data generated by mobile communication users. According to the traffic distributions produced by base stations, we take use of factor analysis method to elaborate the spatiotemporal characteristics. In this paper, we select eight base stations for subsequence study. Firstly, Pearson correlation coefficient and KMO indicator are used to decide whether the data is suitable for factor analysis. Secondly, we use principal component analysis (PCA) method to extract factors and only maintain three factors according to the eigenvalue of correlation matrix. Finally, combining the rotated factor loadings and factor scores, the three factors that we extracted can well explain the characters of business quarter, residential area and information technology (IT) zone in deferent time periods.

Original language	English
Title of host publication	2018 27th Wireless and Optical Communication Conference, WOCC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781538649596
DOIs	https://doi.org/10.1109/WOCC.2018.8372724
State	Published - 4 Jun 2018
Externally published	Yes
Event	27th Wireless and Optical Communication Conference, WOCC 2018 - Hualien, Taiwan, Province of China Duration: 30 Apr 2018 → 1 May 2018

Publication series

Name	2018 27th Wireless and Optical Communication Conference, WOCC 2018

Conference

Conference	27th Wireless and Optical Communication Conference, WOCC 2018
Country/Territory	Taiwan, Province of China
City	Hualien
Period	30/04/18 → 1/05/18

Keywords

CDR
Factor Analysis
Loading Rotation
PCA
User Behavior Analysis

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10.1109/WOCC.2018.8372724

Cite this

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title = "Factor analysis on call detail record",

abstract = "We analyze the call detail record (CDR) data generated by mobile communication users. According to the traffic distributions produced by base stations, we take use of factor analysis method to elaborate the spatiotemporal characteristics. In this paper, we select eight base stations for subsequence study. Firstly, Pearson correlation coefficient and KMO indicator are used to decide whether the data is suitable for factor analysis. Secondly, we use principal component analysis (PCA) method to extract factors and only maintain three factors according to the eigenvalue of correlation matrix. Finally, combining the rotated factor loadings and factor scores, the three factors that we extracted can well explain the characters of business quarter, residential area and information technology (IT) zone in deferent time periods.",

keywords = "CDR, Factor Analysis, Loading Rotation, PCA, User Behavior Analysis",

author = "Qingli Ma and Wen Wang and Qing Yao and Jingdi Zhou and Lei Quo",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 27th Wireless and Optical Communication Conference, WOCC 2018 ; Conference date: 30-04-2018 Through 01-05-2018",

year = "2018",

month = jun,

day = "4",

doi = "10.1109/WOCC.2018.8372724",

language = "英语",

series = "2018 27th Wireless and Optical Communication Conference, WOCC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--5",

booktitle = "2018 27th Wireless and Optical Communication Conference, WOCC 2018",

}

Ma, Q, Wang, W, Yao, Q, Zhou, J & Quo, L 2018, Factor analysis on call detail record. in 2018 27th Wireless and Optical Communication Conference, WOCC 2018. 2018 27th Wireless and Optical Communication Conference, WOCC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 27th Wireless and Optical Communication Conference, WOCC 2018, Hualien, Taiwan, Province of China, 30/04/18. https://doi.org/10.1109/WOCC.2018.8372724

Factor analysis on call detail record. / Ma, Qingli; Wang, Wen; Yao, Qing et al.
2018 27th Wireless and Optical Communication Conference, WOCC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-5 (2018 27th Wireless and Optical Communication Conference, WOCC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Factor analysis on call detail record

AU - Ma, Qingli

AU - Wang, Wen

AU - Yao, Qing

AU - Zhou, Jingdi

AU - Quo, Lei

PY - 2018/6/4

Y1 - 2018/6/4

N2 - We analyze the call detail record (CDR) data generated by mobile communication users. According to the traffic distributions produced by base stations, we take use of factor analysis method to elaborate the spatiotemporal characteristics. In this paper, we select eight base stations for subsequence study. Firstly, Pearson correlation coefficient and KMO indicator are used to decide whether the data is suitable for factor analysis. Secondly, we use principal component analysis (PCA) method to extract factors and only maintain three factors according to the eigenvalue of correlation matrix. Finally, combining the rotated factor loadings and factor scores, the three factors that we extracted can well explain the characters of business quarter, residential area and information technology (IT) zone in deferent time periods.

AB - We analyze the call detail record (CDR) data generated by mobile communication users. According to the traffic distributions produced by base stations, we take use of factor analysis method to elaborate the spatiotemporal characteristics. In this paper, we select eight base stations for subsequence study. Firstly, Pearson correlation coefficient and KMO indicator are used to decide whether the data is suitable for factor analysis. Secondly, we use principal component analysis (PCA) method to extract factors and only maintain three factors according to the eigenvalue of correlation matrix. Finally, combining the rotated factor loadings and factor scores, the three factors that we extracted can well explain the characters of business quarter, residential area and information technology (IT) zone in deferent time periods.

KW - CDR

KW - Factor Analysis

KW - Loading Rotation

KW - PCA

KW - User Behavior Analysis

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U2 - 10.1109/WOCC.2018.8372724

DO - 10.1109/WOCC.2018.8372724

M3 - 会议稿件

AN - SCOPUS:85049396875

T3 - 2018 27th Wireless and Optical Communication Conference, WOCC 2018

SP - 1

EP - 5

BT - 2018 27th Wireless and Optical Communication Conference, WOCC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 27th Wireless and Optical Communication Conference, WOCC 2018

Y2 - 30 April 2018 through 1 May 2018

ER -

Factor analysis on call detail record

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Keywords

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