[논문]시계열 군집분석을 통한 디지털 음원의 순위 변화 패턴 분류

유인진; 박도형

doi:10.13088/jiis.2020.26.3.171

시계열 군집분석을 통한 디지털 음원의 순위 변화 패턴 분류
Derivation of Digital Music's Ranking Change Through Time Series Clustering 원문보기

지능정보연구 = Journal of intelligence and information systems, v.26 no.3, 2020년, pp.171 - 191

유인진 (국민대학교 비즈니스 IT 전문대학원) , 박도형 (국민대학교 경영정보학부)

초록
AI-Helper

본 연구는 현대 사회에서 가장 가치 있는 문화자산이자 한류의 흐름에서 특히 중요한 위치를 차지하는 디지털 음악에 초점을 두었다. 디지털 음악에 대하여 공신력 있는 음원 차트인 '가온 차트'에 진입한 음원들의 73주간 순위 변화를 수집하였으며 유사한 특징을 가지는 패턴들로 분류하였다. 이후 각 순위 변화 패턴으로부터 주목할 만한 특징에 대한 설명적 분석을 수행하였다. 구체적으로 음원에 대한 신뢰도 이슈가 발생하기 이전 기간의 국내 발매된 디지털 음원들로 한정하여 시점을 일치시킨 후 시계열 군집분석을 통해 패턴을 도출하고자 하였다. 데이터 수집과 전처리를 통하여 742건의 중복되지 않는 음원들을 확보하였고, 시계열 순위 변화에 대한 시계열 군집분석 결과 16개의 패턴들이 도출되었다. 이후 도출된 패턴들을 기반으로 '스테디셀러'와 '원 히트 원더'의 두 가지 유형의 대표적인 패턴을 확인하였다. 나아가 두 패턴에 대하여 차트 내에서 음원의 생존 기간과 음원 순위에 관점에서 다섯 가지의 세분화된 패턴으로 분류하였다. 각 패턴들이 가지는 중요한 특징들은 다음과 같다. 원 히트 원더형 패턴에서 아티스트의 슈퍼스타 효과와 편승효과가 강하게 나타났으며, 소비자들의 디지털 음원 선택에 강한 영향을 미친다는 것을 확인하였다. 나아가 스테디셀러형 패턴을 통해서 매우 오랜시간 소비자들의 선택을 받는 음원들을 확인하였고, 소비자의 니즈를 관통하며 가장 많은 선택을 받는 음원들이 오히려 원 히트 원더형 패턴이 아니라 스테디셀러: 중기 패턴에 포진하고 있음을 확인하였다. 특히 주목할 만한 점은 스테디셀러형 패턴을 통해 기존의 패턴과는 상반되는 '차트 역주행' 현상을 확인했다는 것이다. 본 연구는 디지털 음원을 중심으로 상대적으로 소외되었던 분야인 시간의 흐름에 따른 음원의 순위 변화에 초점을 두었고, 음원의 흥행과 순위를 예측하는 것이 아니라 순위 변화의 패턴을 세분화함으로써 음원 연구에 대한 새로운 접근을 시도하였다는 점에서 의의가 있다.

Abstract ▼ AI-Helper

This study focused on digital music, which is the most valuable cultural asset in the modern society and occupies a particularly important position in the flow of the Korean Wave. Digital music was collected based on the "Gaon Chart," a well-established music chart in Korea. Through this, the changes in the ranking of the music that entered the chart for 73 weeks were collected. Afterwards, patterns with similar characteristics were derived through time series cluster analysis. Then, a descriptive analysis was performed on the notable features of each pattern. The research process suggested by this study is as follows. First, in the data collection process, time series data was collected to check the ranking change of digital music. Subsequently, in the data processing stage, the collected data was matched with the rankings over time, and the music title and artist name were processed. Each analysis is then sequentially performed in two stages consisting of exploratory analysis and explanatory analysis. First, the data collection period was limited to the period before 'the music bulk buying phenomenon', a reliability issue related to music ranking in Korea. Specifically, it is 73 weeks starting from December 31, 2017 to January 06, 2018 as the first week, and from May 19, 2019 to May 25, 2019. And the analysis targets were limited to digital music released in Korea. In particular, digital music was collected based on the "Gaon Chart", a well-known music chart in Korea. Unlike private music charts that are being serviced in Korea, Gaon Charts are charts approved by government agencies and have basic reliability. Therefore, it can be considered that it has more public confidence than the ranking information provided by other services. The contents of the collected data are as follows. Data on the period and ranking, the name of the music, the name of the artist, the name of the album, the Gaon index, the production company, and the distribution company were collected for the music that entered the top 100 on the music chart within the collection period. Through data collection, 7,300 music, which were included in the top 100 on the music chart, were identified for a total of 73 weeks. On the other hand, in the case of digital music, since the cases included in the music chart for more than two weeks are frequent, the duplication of music is removed through the pre-processing process. For duplicate music, the number and location of the duplicated music were checked through the duplicate check function, and then deleted to form data for analysis. Through this, a list of 742 unique music for analysis among the 7,300-music data in advance was secured. A total of 742 songs were secured through previous data collection and pre-processing. In addition, a total of 16 patterns were derived through time series cluster analysis on the ranking change. Based on the patterns derived after that, two representative patterns were identified: 'Steady Seller' and 'One-Hit Wonder'. Furthermore, the two patterns were subdivided into five patterns in consideration of the survival period of the music and the music ranking. The important characteristics of each pattern are as follows. First, the artist's superstar effect and bandwagon effect were strong in the one-hit wonder-type pattern. Therefore, when consumers choose a digital music, they are strongly influenced by the superstar effect and the bandwagon effect. Second, through the Steady Seller pattern, we confirmed the music that have been chosen by consumers for a very long time. In addition, we checked the patterns of the most selected music through consumer needs. Contrary to popular belief, the steady seller: mid-term pattern, not the one-hit wonder pattern, received the most choices from consumers. Particularly noteworthy is that the 'Climbing the Chart' phenomenon, which is contrary to the existing pattern, was confirmed through the steady-seller pattern. This study focuses on the change in

주제어

질의응답

핵심어	질문	논문에서 추출한 답변
	디지털 음악은 무엇을 의미하나?	이러한 현재 한류의 흐름에서는 특히 디지털 음악이 중요한 위치를 차지한다. 이때 디지털 음악은 디지털화되어 유통되는 파일 형식의 음악을 의미한다. 디지털화, 그리고 파일 형식이 라는 특성상 디지털 음악은 아날로그 음악과는 달리 네트워크를 통하여 다양한 포맷으로 변환될 수 있으며, PC, 스마트폰, 태블릿 등의 기기의 한계를 두지 않는다.
	군집 분석은 무엇을 의미하나?	군집 분석은 표본들 간의 유사도에 기반하여 동질성이 높은 표본들을 하나의 군집으로 분류하는 탐색적 분석을 의미한다(Wilks, 2011). 데이터 마이닝 기법들 중 대표적인 비지도학습인 군집 분석은 분석 과정에서 동질적인 표본끼리는 동일한 군집에 배치하고, 이질적인 표본들간에는 서로 다른 군집에 속하도록 배치한다.
	군집 분석은 어떤 강점이 있는가?	군집 분석의 주요 목적은 데이터들 간의 구조적 특성을 파악하기 위하여 주로 사용된다. 즉, 군집화를 통하여 복잡한 데이터들 몇 개의 군집으로 나타낼 수 있으며, 나아가 데이터 집단의 대표적인 특성이나 유형을 파악하기 용이하다는 강점이 있다(Bishop, 2006).

참고문헌 (46)

Adler, M., "Stardom and talent," The American economic review, Vol.75, No.1, (1985), 208-212.

상세보기
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Bhattacharjee, S., R. D. Gopal, K. Lertwachara, J. R. Marsden, amd R. Telang, "The effect of digital sharing technologies on music markets: A survival analysis of albums on ranking charts," Management Science, Vol.53, No.9, (2007), 1359-1374.

상세보기
Bishop, C. M., Pattern recognition and machine learning, (2006), springer.
Cheom A. -Y., "A Study on the Success Factors of Popular Music Sources," Master's Thesis, Sungkyunkwan University Graduate School, 2018.
Cuturi, M., "Fast global alignment kernels," In Proceedings of the 28th international conference on machine learning (ICML-11), (2011), 929-936.
Han, E. J., "A Study on the Relationship between Popularity of Advertising Background Music and Musical Elements," Master's Thesis, Graduate School of Journalism and Broadcasting, Chung-Ang University, 2004.
Hyeon, C. -M., "A Study on the Success Factors of Digital Sound Source," Master's Thesis, Graduate School, Chung-Ang University, 2014.
Jo, B. C. and H. C. Sim, "Success Factor Analysis of K-POP and A Study on sustainable Korean Wave - Focus on Smart Media based on Realistic Contents," The Journal of the Korea Contents Association, Vol.13, No.5, (2013), 90-102.

원문보기 상세보기
Jo, S. M., "Searching for revitalization of global expansion through social networks (SNS) in the digital music market," Master's thesis, Graduate School of Joong-bu University, 2013.
Jung, E. Y., "A Study on New Ecosystem Model through Case Comparison of Korean, US, and Japanese Digital Music Markets," Ph.D. Thesis, Graduate School of Arts, Chung-Ang University, 2016.
Kim, H. J., "Star Power Analysis of Korean Movie Stars," Korea Association for Cultural Economics, Vol.1, No.1, (1998), 165-200.
Kim, H. -S., "A Study on the Influence and Development Direction of Digital Technology on the Music Industry," Master's Thesis, Graduate School of Media and Public Relations, Yonsei University, 2002.
Lee, S. M., "A Study on Marketing of Popular Music Using SNS," Master's Thesis, Graduate School of Arts, Chung-Ang University, 2019.
Lee, W. G., I. S. Han, and Y. M. Yoon, "Prediction of Record Charts Progress," Journal of Korean Institute of Information Technology, Vol.12, No.3, (2014), 121-128.

상세보기
Ministry of Culture, Sports and Tourism, 2018 Overseas Korean Wave Survey Report, Ministry of Culture, Sports and Tourism, 2018.
Montero, P., Vilar, J. A., "TSclust: An R package for time series clustering," Journal of Statistical Software, Vol.62, No.1, (2014), 1-43.
Moon, H. -C., "A Study on Changes in Market Environment by Convergence of Music Industry and Digital Technology," Master's Thesis, Graduate School of Business, Dankook University, 2011.
Pachet, F., "Hit Song Science. Pitch, harmony, and neural nets: A psychological perspective," Music and Data Mining, (2011), 306-314.
Pachet, F., and P. Roy, "Hit Song Science Is Not Yet a Science," In ISMIR, (2008), 355-360.
Ryu, J. -S., "A Study on the Current Status of the Digital Music Market," Master's Thesis, Graduate School of Culture and Arts, Dankook University, 2014.
Strobl, E. A., and C. Tucker, "The dynamics of chart success in the UK pre-recorded popular music industry," Journal of Cultural Economics, Vol.24, No.2, (2000), 113-134.

상세보기
Wilks, D. S., Statistical methods in the atmospheric sciences, Vol.100, (2011), Academic press.
Adler, M., "Stardom and talent," The American economic review, Vol.75, No.1, (1985), 208-212.

상세보기
Berry, M., Linoff, G., Data mining techniques: For marketing, (1997), Sales and Marketing Support.
Bhattacharjee, S., R. D. Gopal, K. Lertwachara, J. R. Marsden, amd R. Telang, "The effect of digital sharing technologies on music markets: A survival analysis of albums on ranking charts," Management Science, Vol.53, No.9, (2007), 1359-1374.

상세보기
Bishop, C. M., Pattern recognition and machine learning, (2006), springer.
Cheom A. -Y., "A Study on the Success Factors of Popular Music Sources," Master's Thesis, Sungkyunkwan University Graduate School, 2018.
Cuturi, M., "Fast global alignment kernels," In Proceedings of the 28th international conference on machine learning (ICML-11), (2011), 929-936.
Han, E. J., "A Study on the Relationship between Popularity of Advertising Background Music and Musical Elements," Master's Thesis, Graduate School of Journalism and Broadcasting, Chung-Ang University, 2004.
Hyeon, C. -M., "A Study on the Success Factors of Digital Sound Source," Master's Thesis, Graduate School, Chung-Ang University, 2014.
Jo, B. C. and H. C. Sim, "Success Factor Analysis of K-POP and A Study on sustainable Korean Wave - Focus on Smart Media based on Realistic Contents," The Journal of the Korea Contents Association, Vol.13, No.5, (2013), 90-102.

원문보기 상세보기
Jo, S. M., "Searching for revitalization of global expansion through social networks (SNS) in the digital music market," Master's thesis, Graduate School of Joong-bu University, 2013.
Jung, E. Y., "A Study on New Ecosystem Model through Case Comparison of Korean, US, and Japanese Digital Music Markets," Ph.D. Thesis, Graduate School of Arts, Chung-Ang University, 2016.
Kim, H. J., "Star Power Analysis of Korean Movie Stars," Korea Association for Cultural Economics, Vol.1, No.1, (1998), 165-200.
Kim, H. -S., "A Study on the Influence and Development Direction of Digital Technology on the Music Industry," Master's Thesis, Graduate School of Media and Public Relations, Yonsei University, 2002.
Lee, S. M., "A Study on Marketing of Popular Music Using SNS," Master's Thesis, Graduate School of Arts, Chung-Ang University, 2019.
Lee, W. G., I. S. Han, and Y. M. Yoon, "Prediction of Record Charts Progress," Journal of Korean Institute of Information Technology, Vol.12, No.3, (2014), 121-128.

상세보기
Ministry of Culture, Sports and Tourism, 2018 Overseas Korean Wave Survey Report, Ministry of Culture, Sports and Tourism, 2018.
Montero, P., Vilar, J. A., "TSclust: An R package for time series clustering," Journal of Statistical Software, Vol.62, No.1, (2014), 1-43.
Moon, H. -C., "A Study on Changes in Market Environment by Convergence of Music Industry and Digital Technology," Master's Thesis, Graduate School of Business, Dankook University, 2011.
Pachet, F., "Hit Song Science. Pitch, harmony, and neural nets: A psychological perspective," Music and Data Mining, (2011), 306-314.
Pachet, F., and P. Roy, "Hit Song Science Is Not Yet a Science," In ISMIR, (2008), 355-360.
Ryu, J. -S., "A Study on the Current Status of the Digital Music Market," Master's Thesis, Graduate School of Culture and Arts, Dankook University, 2014.
Strobl, E. A., and C. Tucker, "The dynamics of chart success in the UK pre-recorded popular music industry," Journal of Cultural Economics, Vol.24, No.2, (2000), 113-134.

상세보기
Wilks, D. S., Statistical methods in the atmospheric sciences, Vol.100, (2011), Academic press.

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