A Resamping Approach for Customer Gender Prediction Based on E-Commerce Data

2017 ◽  
Vol 3 (1) ◽  
pp. 76 ◽  
Author(s):  
Duong Tran Duc ◽  
Pham Bao Son ◽  
Tan Hanh ◽  
Le Truong Thien
Keyword(s):  
Data Mining ◽  
Bayesian Network ◽  
Web Applications ◽  
Service Providers ◽  
Main Idea ◽  
Classification Methods ◽  
Cost Sensitive Learning ◽  
Network Method ◽  
Demographic Attributes ◽  
Privacy Issues

Demographic attributes of customers such as gender, age, etc. provide the important information for e-commerce service providers in marketing, personalization of web applications. However, the online customers often do not provide this kind of information due to the privacy issues and other reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, the products viewed, etc. The main idea is that we extract the features from catalog viewing information and employ the classification methods to predict the gender of the viewers. The experiments were conducted on the datasets provided by the PAKDD’15 Data Mining Competition and obtained the promising results with a simple feature design, especially with the Bayesian Network method along with other supporting techniques such as resampling, cost-sensitive learning, boosting etc.

Clustering and Bayesian Networks

2020 ◽  
pp. 50-73
Author(s):  
Bhanu Chander
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Bayesian Network ◽  
Learning Communities ◽  
Mining Machine ◽  
Belief Networks ◽  
Classification Methods ◽  
Probabilistic Information ◽  
Training Stage ◽  
Relationship Of

The goal of this chapter is to present an outline of clustering and Bayesian schemes used in data mining, machine learning communities. Standardized data into sensible groups is the preeminent mode of understanding as well as learning. A cluster constitutes a set regarding entities that are alike and entities from different clusters are not alike. Representing data by fewer clusters inevitably loses certain fine important information but achieves better simplification. There is no training stage in clustering; mostly, it's used when the classes are not well-known. Bayesian network is one of the best classification methods and is frequently used. Generally, Bayesian network is a form of graphical probabilistic representation model that consists of a set of interconnected nodes, where each node represents a variable, and inter-link connection represents a causal relationship of those variables. Belief networks are graph symbolized models that successfully model familiarity via transmitting probabilistic information to a variety of assumptions.

Stability-based Dynamic Bayesian Network method for dynamic data mining

2019 ◽  
Vol 77 ◽  
pp. 283-310 ◽  
Author(s):  
Mohamed Naili ◽  
Mustapha Bourahla ◽  
Makhlouf Naili ◽  
AbdelKamel Tari
Keyword(s):  
Data Mining ◽  
Bayesian Network ◽  
Dynamic Bayesian Network ◽  
Dynamic Data ◽  
Network Method

A Survey on Privacy Preservation in Location-Based Mobile Business

2021 ◽  
Vol 13 (1) ◽  
pp. 20-39
Author(s):  
Ahmed Aloui ◽  
Okba Kazar
Keyword(s):  
Privacy Protection ◽  
Privacy Preservation ◽  
Service Providers ◽  
Personal Information ◽  
Third Party ◽  
Mobile Business ◽  
Protection Measures ◽  
System Architectures ◽  
Protection Goals ◽  
Privacy Issues

In mobile business (m-business), a client sends its exact locations to service providers. This data may involve sensitive and private personal information. As a result, misuse of location information by the third party location servers creating privacy issues for clients. This paper provides an overview of the privacy protection techniques currently applied by location-based mobile business. The authors first identify different system architectures and different protection goals. Second, this article provides an overview of the basic principles and mechanisms that exist to protect these privacy goals. In a third step, the authors provide existing privacy protection measures.

scholarly journals The Challenges of Institutional Distance: Data Privacy Issues in Cloud Computing

2018 ◽  
Vol 24 (1) ◽  
pp. 161-181 ◽  
Author(s):  
Yashar Abed ◽  
Meena Chavan
Keyword(s):  
Cloud Computing ◽  
Multinational Corporations ◽  
Data Privacy ◽  
Service Providers ◽  
Host Countries ◽  
Case Study Methodology ◽  
Regulatory Constraints ◽  
Cloud Computing Service ◽  
Privacy Issues

Data protection and data privacy are significant challenges in cloud computing for multinational corporations. There are no standard laws to protect data across borders. The institutional and regulatory constraints and governance differ across countries. This article explores the challenges of institutional constraints faced by cloud computing service providers in regard to data privacy issues across borders. Through a qualitative case study methodology, this research compares the institutional structure of a few host countries, with regard to data privacy in cloud computing and delineates a relative case study. This article will also review the cloud computing legal frameworks and the history of cloud computing to make the concept more comprehensible to a layman.

scholarly journals The Geometric Brownian Motion of Indosat Telecommunications Daily Stock Price During the Covid-19 Pandemic in Indonesia

2021 ◽  
Vol 2084 (1) ◽  
pp. 012012
Author(s):  
Tiara Shofi Edriani ◽  
Udjianna Sekteria Pasaribu ◽  
Yuli Sri Afrianti ◽  
Ni Nyoman Wahyu Astute
Keyword(s):  
Brownian Motion ◽  
Stock Price ◽  
Service Providers ◽  
Main Idea ◽  
Geometric Brownian Motion ◽  
Government Policies ◽  
Percentage Error ◽  
Price Movement ◽  
Data Movement ◽  
Total Data

Abstract One of the major telecommunication and network service providers in Indonesia is PT Indosat Tbk. During the coronavirus (COVID-19) pandemic, the daily stock price of that company was influenced by government policies. This study addresses stock data movement from February 5, 2020 to February 5, 2021, resulted in 243 data, using the Geometric Brownian motion (GBM). The stochastic process realization of this stock price fluctuates and increases exponentially, especially in the 40 latest data. Because of this situation, the realization is transformed into log 10 and calculated its return. As a result, weak stationary in variance is obtained. Furthermore, only data from December 7, 2020 to February 5, 2021 fulfill the GBM assumption of stock price return, as R t 1 * , t 1 * = 1 , 2 , 3 , … , 40 . The main idea of this study is adding datum one by one as much as 10% – 15% of the total data R t 1 * , starting from December 4, 2020 backwards. Following this procedure, and based on the 3% < p-value < 10%, the study shows that its datum can be included in R t 1 * , so t 1 * = − 4. − 3 , − 2 , … , 40 and form five other data groups, R t 2 * , … , R t 6 * . Considering Mean Absolute Percentage Error (MAPE) and amount of data from each group, R t 6 * is selected for modelling. Thus, GBM succeeded in representing the stock price movement of the second most popular Indonesian telecommunication company during COVID-19 pandemic.

scholarly journals Analysis of Mobile Service Providers Performance Using Naive Bayes Data Mining Technique

2018 ◽  
Vol 8 (6) ◽  
pp. 5153
Author(s):  
M. A. Burhanuddin ◽  
Ronizam Ismail ◽  
Nurul Izzaimah ◽  
Ali Abdul-Jabbar Mohammed ◽  
Norzaimah Zainol
Keyword(s):  
Data Mining ◽  
Data Science ◽  
Service Providers ◽  
Telecommunication Service ◽  
Mobile Service ◽  
Business Growth ◽  
Data Mining Technique ◽  
R Language ◽  
Mining Technique ◽  
Industry Standard

Recently, the mobile service providers have been growing rapidly in Malaysia. In this paper, we propose analytical method to find best telecommunication provider by visualizing their performance among telecommunication service providers in Malaysia, i.e. TM Berhad, Celcom, Maxis, U-Mobile, etc. This paperuses data mining technique to evaluate the performanceof telecommunication service providers using their customers feedback from Twitter Inc. It demonstrates on how the system could process and then interpret the big data into a simple graph or visualization format. In addition, build a computerized tool and recommend data analytic model based on the collected result. From prepping the data for pre-processing until conducting analysis, this project is focusing on the process of data science itself where Cross Industry Standard Process for Data Mining (CRISP-DM) methodology will be used as a reference. The analysis was developed by using R language and R Studio packages. From the result, it shows that Telco 4 is the best as it received highest positive scores from the tweet data. In contrast, Telco 3 should improve their performance as having less positive feedback from their customers via tweet data. This project bring insights of how the telecommunication industries can analyze tweet data from their customers. Malaysia telecommunication industry will get the benefit by improving their customer satisfaction and business growth. Besides, it will give the awareness to the telecommunication user of updated review from other users.

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