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A customer segmentation framework for targeted marketing in telecommunication

Anahita Namvar, Mehdi Ghazanfari, Mohsen Naderpour
2017 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)  
In this research, we adopt behavior and beneficial segmentation in a two-dimensional framework to segment customers.  ...  Therefore, a data-driven segmentation approach can support marketing strategies to tailor their marketing plans.  ...  Clustering for Telecom Clustering is one of the data mining techniques that has been widely used for customer segmentation, targeted marketing, and cross-selling.  ... 
doi:10.1109/iske.2017.8258803 dblp:conf/iske/NamvarGN17 fatcat:yrkc24gaifdj7fulrzz55ihwvy

STUDYING PRODUCT QUALITY BY EXPLORING CREDIT CARD CUSTOMERS BEHAVIOUR VIA DATA MINING TECHNIQUES

Ladan Hassani, Ehsan Taati
2020 International Journal for Quality Research  
First, high-quality customers were selected using data-mining tools (Kmeans/C&RT algorithm); Results show 93 high-quality customers.  ...  Results show that 43% and 57% customers use the credit-card as a loan-card and revolving-credit respectively.  ...  In this research, C&RT algorithm was used to replace missing data. 5 Management Extreme and Outlier At this point, the anomaly detection method was used to identify and manage Extremes and Outliers  ... 
doi:10.24874/ijqr14.01-11 fatcat:ny2doa4u7facvphoaqu6enfm64

DATA MINING IN BANKING AND ITS APPLICATIONS-A REVIEW

Pulakkazhy
2013 Journal of Computer Science  
Interesting patterns and knowledge can be mined from this huge volume of data that in turn can be used for this decision making process.  ...  This article explores and reviews various data mining techniques that can be applied in banking areas. It provides an overview of data mining techniques and procedures.  ...  Clustering methods can be used to classify transactions and outliers can be analyzed for frauds (Dheepa and Dhanapal, 2009) .  ... 
doi:10.3844/jcssp.2013.1252.1259 fatcat:4gnn4gxsyzfe3ivkgj5fpkoocq

Using Unsupervised Machine Learning Techniques for Behavioral-based Credit Card Users Segmentation in Africa

Eric Umuhoza, Dominique Ntirushwamaboko, Jane Awuah, Beatrice Birir
2020 SAIEE Africa Research Journal  
We acknowledge the big role played by the CIB and its staff, especially Ms. Nelly Youssef and Mr. Andrew Raafat of the analytics and big data lab for their tremendous assistance during the project.  ...  phase detected and removed outliers.  ...  In our case, the extreme observations were detected and removed using the interquartile method. B.  ... 
doi:10.23919/saiee.2020.9142602 fatcat:42ynwtzg4rfdxoewawtjnlgbly

Managing the Process of Segmentation on the Mobile Phone Subscribers

Rodrigue Carlos Nana Mbinkeu, Domenico Beneventano
2015 International Journal of Recent Contributions from Engineering, Science & IT  
Our paper proposes a set of techniques to analyze and design tools that manages the process of data acquisition, data cleaning and selection of the segmentation algorithm.  ...  The segmentation will identify and select the subscribers most likely to respond favorably to offers.  ...  The knowledge gathered can be used for different purposes: call individual clients, make offers tailored to target groups, predict customer behavior like as a churn [10] .  ... 
doi:10.3991/ijes.v3i2.4491 fatcat:bwmnqcm7orbxlfzjb5zbk7mfpm

Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees

Mirjana Pejić Bach, Jasmina Pivar, Božidar Jaković
2021 Journal of Risk and Financial Management  
In the second stage, k-means cluster analysis is used to identify market segments for which chi-square analysis is applied to detect the clusters with the highest churn ratio.  ...  The contribution of this paper resides in the development of the structured approach to churn management using clustering and classification, which was tested on the churn dataset with a rich variable  ...  Zheng and Liu (2020) used a hierarchical locality sensitive hashing-local outlier factor scheme for anomalous customer behavior detection and k-means clustering analysis on the real telecommunication  ... 
doi:10.3390/jrfm14110544 fatcat:7gde5zpinbbgjijfvuuktx323y

Opportunities for automated inference with data in tomorrow's communication networks and services

Tin Kam Ho, Thomas Bengtsson
2009 Bell Labs technical journal  
Discovery of hidden correlations between network health and network state indicators can help prevent failures and improve system design.  ...  We discuss example uses of automated inference with data along these themes, and highlight the challenges and opportunities to be addressed by continuous research.  ...  Acknowledgements We thank Jin Cao, Aiyou Chen, Patricia Scanlon, and Francis Zane for sharing many details and insights from their work. Jin and Aiyou also kindly provided the plots in Figure 2 .  ... 
doi:10.1002/bltj.20397 fatcat:jkubonfrsbbxbn4i7gcznep65u

Segmentation of Retail Mobile Market Using HMS Algorithm

Koyi Anusha, Yashaswini C, Manishankar S
2016 International Journal of Electrical and Computer Engineering (IJECE)  
Data mining includes a wide variety of techniques and algorithm which can be effectively used in the process of market analysis.  ...  mobile market in to various customer and product groups and also provides a prediction and suggestion system for company as well as customer.  ...  "use of data mining techniques to improve the effectiveness of sales and marketing" This paper proposes cluster association mining approach to classify associated patterns of sale and classify stock data  ... 
doi:10.11591/ijece.v6i4.10295 fatcat:tcdcmq7rzzgl3apzfj65sjvhs4

Data science and AI in FinTech: An overview [article]

Longbing Cao, Qiang Yang, Philip S. Yu
2021 arXiv   pre-print
Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems.  ...  Smart FinTech is the new-generation FinTech, largely inspired and empowered by data science and new-generation AI and (DSAI) techniques.  ...  pattern Trading behavior analysis, abnormal trading analysis, outlier detection, investor relation analysis, customer profiling, high-utility trading pattern analysis, and cross-market trading behavior  ... 
arXiv:2007.12681v2 fatcat:jntzuwaktjg2hmmjypi5lvyht4

The Influence of Data Preparation on Outlier Detection in Driveability Data

Andreas Ramsauer, Petra Martina Baumann, Cornelia Lex
2021 SN Computer Science  
Parameters of the investigated techniques are varied and the effect on the detected outliers is discussed in detail.  ...  With the variables of interest for driveability evaluation being of highly different magnitude, data scaling methods, also referred to as data normalization methods, are applied and the impact on the outlier  ...  By varying the parameter, the outlier threshold can be customized with respect to the subjective preferences leading to custom tailorable results.  ... 
doi:10.1007/s42979-021-00607-7 fatcat:il5oflnkvrea7kwuccsgrjckuu

HiCS: High Contrast Subspaces for Density-Based Outlier Ranking

Fabian Keller, Emmanuel Muller, Klemens Bohm
2012 2012 IEEE 28th International Conference on Data Engineering  
Outlier mining is a major task in data analysis. Outliers are objects that highly deviate from regular objects in their local neighborhood.  ...  and provides enhanced quality for outlier ranking.  ...  In conclusion, any improvement in either of these steps will lead to an improvement in the overall outlier detection quality.  ... 
doi:10.1109/icde.2012.88 dblp:conf/icde/KellerMB12 fatcat:ouotyhngrnaglcoi5g23ij7dqi

SELECTING CLASSIFICATION AND CLUSTERING TOOLS FOR ACADEMIC SUPPORT

2007 Issues in Information Systems  
Classification and clustering are powerful and popular data mining techniques. Organizations use them to capture information, retain customers, and improve business performance.  ...  This paper presents a method for selecting data mining software for an academic environment based on its classification and clustering tools.  ...  It enables the user to identify and remove outliers from data sets. Users can eliminate rare values in class variables and/or extreme values in interval variables.  ... 
doi:10.48009/2_iis_2007_265-272 fatcat:55lkwbqskrbvbgb4jz3dhngoya

Big data analytics for preventive medicine

Muhammad Imran Razzak, Muhammad Imran, Guandong Xu
2019 Neural computing & applications (Print)  
We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and  ...  How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data?  ...  Clustering-based Multivariate Gaussian Outlier Score (CMGOS) is another extension of cluster-based anomaly detection.  ... 
doi:10.1007/s00521-019-04095-y pmid:32205918 pmcid:PMC7088441 fatcat:x52upnuwbjdchkyb7hog5pvawm

Investigation on the Impact of Leadership Styles Using Data Mining Techniques [chapter]

Waseem Ahmad, Muhammad Akhtaruzamman, Uswa Zahra, Chandan Ohri, Binu Ramakrishnan
2018 Leadership  
A quantitative analysis was conducted on collected data using statistical methods (such as correlation and regression analysis) and state-of-the-art data mining techniques (rule-based approaches and decision  ...  The data mining techniques were used to extract hidden trends and patterns in the data to report various ways to increase the employee outcomes by fine-tuning leadership styles.  ...  Customer segmentation, anomaly detection and identifying association rules in buying patterns of customers are few examples of clustering algorithms [14] .  ... 
doi:10.5772/intechopen.78660 fatcat:e4353ulhifeilgn7yaeh2mkgha

Data-Driven Digital Twins for Technical Building Services Operation in Factories: A Cooling Tower Case Study

Christine Blume, Stefan Blume, Sebastian Thiede, Christoph Herrmann
2020 Journal of Manufacturing and Materials Processing  
It aims to improve system understanding and performance prediction as essentials for a successful operational management.  ...  Step by step, the workflow is explained including a tailored data pre-processing, transformation and aggregation as well as feature selection procedure.  ...  detect outliers.  ... 
doi:10.3390/jmmp4040097 doaj:3528e501a163475483ec1526049a6258 fatcat:fa3ro5vuajhf7d4pk52xiarjr4
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