Mining class outliers: concepts, algorithms and applications in CRM
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Lee et al. (2018) defined the period during which 75% of customers were continuously unconnected as a churn section for PC game services.
“Lee, Eunjo, et al. (2018) defined the period during which 75% of customers were continuously unconnected as a churn section by taking into consideration the characteristics of PC game services [17].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [17]
If the time period during which a customer completely churns is selected as a time window, the period for churn definition exponentially increases and it does not provide any gain in terms of business.
“If the time period during which a customer completely churns is selected as a time window, the period for churn definition exponentially increases and it does not provide any gain in terms of business as changing the will of the customers who want to churn is deemed impossible [17].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [17]
Internet services interpret log-in as retention and unconnected access for a certain period as churn.
“Therefore, the Internet service interprets the log-in as prolonged, that is, the retention of the service, and interprets unconnected access for a certain period of time as churn [17].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [17]
According to Lee et al. (2018), the period during which 75% or more customers are continuously unconnected is 14 weeks, and if a customer is unconnected for 14 weeks, he/she is regarded as churn.
“According to Fig. 2, the period during which customers were unconnected more than 75% was 14 According to the figure, the period during which 75% or more customers are continuously unconnected is 14 weeks.Therefore, if a customer is unconnected for 14 weeks, he/she is regarded as churn and this period is a time window section.”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [17]
Cites
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207 · see citing works
FP-outlier: Frequent pattern based outlier detection
He, Z., Xu, X., Huang, Z., & Deng, S. (2005) · Computer Science and Information Systems · doi:10.2298/csis0501103h · Cited by 188 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Contextual anomaly detection framework for big sensor data
Hayes, M. A., & Capretz, M. A. (2015) · Journal of Big Data · doi:10.1186/s40537-014-0011-y · Cited by 149 · see citing works
A Survey on Churn Analysis in Various Business Domains
Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3042657 · Cited by 108 · see citing works
“The analytical CRM is focused on developing databases and resources containing customer characteristics and attitudes [24], [25], [30].”
Background
- Authors
- He, Z., Xu, X., Huang, J. Z., & Deng, S.
- Affiliations
- Harbin Institute of Technology (CN)
- University of Hong Kong (HK)
- Corresponding author
- He, Zengyou
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2004-11
- Volume
- 27
- Issue
- 4
- Pages
- 681-697
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2097006579
- Cited by
- 79
- References
- 2
- Citations by year
- 2025: 1 · 2023: 2 · 2022: 2 · 2020: 4 · 2019: 2 · 2018: 4 · 2017: 4 · 2016: 3 · 2015: 8 · 2014: 2 · 2013: 9 · 2012: 10
- FWCI
- 3.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- outlier detection, class labels, customer relationship management, data mining algorithms, exceptional cases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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