Mining Target-Oriented Fuzzy Correlation Rules to Optimize Telecom Service Management

To optimize telecom service management, it is necessary that information about telecom services is highly related to the most popular telecom service. To this end, we propose an algorithm for mining target-oriented fuzzy correlation rules. In this paper, we show that by using the fuzzy statistics analysis and the data mining technology, the target-oriented fuzzy correlation rules can be obtained from a given database. We conduct an experiment by using a sample database from a telecom service provider in Taiwan. Our work can be used to assist the telecom service provider in providing the appropriate services to the customers for better customer relationship management.Comment: 10 pages, 7 table

Customer Churn Prediction in Telecom Sector: A Survey and way a head

© 2021 International Journal of Scientific & Technology Research. This work is licensed under a Creative Commons Attribution 4.0 International License.The telecommunication (telecom)industry is a highly technological domain has rapidly developed over the previous decades as a result of the commercial success in mobile communication and the internet. Due to the strong competition in the telecom industry market, companies use a business strategy to better understand their customers’ needs and measure their satisfaction. This helps telecom companies to improve their retention power and reduces the probability to churn. Knowing the reasons behind customer churn and the use of Machine Learning (ML) approaches for analyzing customers' information can be of great value for churn management. This paper aims to study the importance of Customer Churn Prediction (CCP) and recent research in the field of CCP. Challenges and open issues that need further research and development to CCP in the telecom sector are exploredPeer reviewe

Customer Segmentation Using Real Transactional Data in E-Commerce Platform: A Case of Online Fashion Bags Shop

Customer segmentation has been widely used in different businesses and plays important rules in customer service. How to get a suitable segmentation based on the real transactional data to fully mining the hidden customer information in the massive data is still a challenge in current e-commerce platforms. This paper develops a customer segmentation model for online shops and uses the real data from a fashion bag store as a case. This paper firstly conducts a data preprocessing to select the main customer features, then it constructs a segmentation model based on the Fuzzy C-Means algorithm, and finally accomplishes a customer prediction model using a probabilistic neural network to estimate new customer’s customer type. The results show that the customer samples are classified into three types, and the prediction accuracy is more than 90%. After that, this paper demonstrates the typical features of each type of customer and compares the new group features with the prior VIP groups. The ANOVA analysis test results show that the new groups have more significant differences than prior VIP groups, which means more effective segmentation results

Data Mining Technique for Predicting Telecommunications Industry Customer Churn Using both Descriptive and Predictive Algorithms

As markets have become increasingly saturated, companies have acknowledged that their business strategies need to focuson identifying those customers who are most likely to churn. It is becoming common knowledge in business, that retainingexisting customers is the best core marketing strategy to survive in industry. In this research, both descriptive and predictivedata mining techniques were used to determine the calling behaviour of subscribers and to recognise subscribers with highprobability of churn in a telecommunications company subscriber database. First a data model for the input data variablesobtained from the subscriber database was developed. Then Simple K-Means and Expected Maximization (EM) clusteringalgorithms were used for the clustering stage, while Decision Stump, M5P and RepTree Decision Tree algorithms were usedfor the classification stage. The best algorithms in both the clustering and classification stages were used for the predictionprocess where customers that were likely to churn were identified.Keywords: customer churn; prediction; clustering; classificatio

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

A model to Predict Churn

This Master Thesis has been performed at Svenska Spel with the aim to detect playing customers probability to churn, i.e. quit their gambling. The first part of the Thesis consists of some previously work done within the field, some facts about Svenska Spel and explanations of used software. The next part describes how the work has been done and the third part give the reader the theory behind the prediction model. The model used for prediction churn is Logistic Regression with related statistical test to investigate and verify the model. Finally, a prediction model and verification results are presented