In this paper we have explained the detailed work done in developing a framework which can be used for the purpose of discovering business intelligence with the help of decision rules induced from the customer reviews of a product or a service posted online. We have explained the analysis for the product Samsung Galaxy S5. Our proposed framework has been designed by collecting the reviews from Samsung home page, preprocessing it and inducing rules by using rough set based LEM2 algorithm. The induced rules would be helpful for Business analyst in understanding the product dimensions, attributes and inherent association among them. © 2014 IEEE.

Tapan Kumar Das

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

Debi Prasanna Acharjya

Department of Computational Intelligence

School of Computer Science and Engineering

Patra M.R.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Business intelligence from online product review-a rough set based rule induction approach

2014 International Conference on Contemporary Computing and Informatics (IC3I)

Hospital ranking is a cumbersome task, as it involves dealing with a large volume of underlying data. Rankings are usually accomplished by comparing different dimensions of quality and services. Even the quality care measurement of a hospital is multi-dimensional: It includes the experience of both clinical care and patient care. In this research, however, the authors focus on ratings based only on customer perception. A framework which consists of two stages—Stage I and Stage II—is designed. In the first stage, the model uses a rough set in a fuzzy approximation space (RSFAS) technique to classify the data; whereas in the second stage, a fuzzy soft set (FSS) technique is employed to generate the rating score. The model is employed for comparing USA hospitals by region using annual HCAHPS survey data. This article shows how ranking of the healthcare institutions can be carried out using the RSFAS (rough set in a fuzzy approximation space) and fuzzy soft set techniques.

International Journal of Healthcare Information Systems and Informatics

A Framework for Ranking Hospitals Based on Customer Perception Using Rough Set and Soft Set Techniques

Machine learning has gained immense popularity in a variety of fields as it has the ability to change the conventional workflow of a process. The abundance of data available serves as the motivation for this. This data can be exploited for a good deal of knowledge. In this article, we focus on operational data of networking devices that are deployed in different locations. This data can be used to predict faults in the devices. Usually, after the deployment of networking devices in customer site, troubleshooting these devices is difficult. Operational data of these devices is needed for this process. Manually analysing the machined produced operational data is tedious and complex due to enormity of data. Using machine learning techniques will be of greater help here as this will help automate the troubleshooting process, avoid human errors and save time for the technical solutions engineers.

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Journal	Data powered by Typeset2014 International Conference on Contemporary Computing and Informatics (IC3I)
Publisher	Data powered by TypesetIEEE
Open Access	No