6,006 research outputs found
An intelligent recommendation system framework for student relationship management
In order to enhance student satisfaction, many services have been provided in order to meet student needs. A recommendation system is a significant service which can be used to assist students in several ways. This paper proposes a conceptual framework of an Intelligent Recommendation System in order to support Student Relationship Management (SRM) for a Thai private university. This article proposed the system architecture of an Intelligent Recommendation System (IRS) which aims to assist students to choose an appropriate course for their studies. Moreover, this study intends to compare different data mining techniques in various recommendation systems and to determine appropriate algorithms for the proposed electronic Intelligent Recommendation System (IRS). The IRS also aims to support Student Relationship Management (SRM) in the university. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification
Application of artificial neural network in market segmentation: A review on recent trends
Despite the significance of Artificial Neural Network (ANN) algorithm to
market segmentation, there is a need of a comprehensive literature review and a
classification system for it towards identification of future trend of market
segmentation research. The present work is the first identifiable academic
literature review of the application of neural network based techniques to
segmentation. Our study has provided an academic database of literature between
the periods of 2000-2010 and proposed a classification scheme for the articles.
One thousands (1000) articles have been identified, and around 100 relevant
selected articles have been subsequently reviewed and classified based on the
major focus of each paper. Findings of this study indicated that the research
area of ANN based applications are receiving most research attention and self
organizing map based applications are second in position to be used in
segmentation. The commonly used models for market segmentation are data mining,
intelligent system etc. Our analysis furnishes a roadmap to guide future
research and aid knowledge accretion and establishment pertaining to the
application of ANN based techniques in market segmentation. Thus the present
work will significantly contribute to both the industry and academic research
in business and marketing as a sustainable valuable knowledge source of market
segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table
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Data Mining for Shopping Centres – Customer Knowledge-Management Framework
Shopping centers are an important part of the UK economy and have been the subject of considerable research. Relying on complex interdependencies between shoppers, retailers and owners, shopping centers are ideal for knowledge management study. Nevertheless, although retailers have been in the forefront of data mining, little has been written on Customer Knowledge Management for shopping centers. In this chapter, the authors aim to demonstrate the possibilities and draw attention to the possible implications of improving customer satisfaction. Aspects of customer knowledge management for shopping centers are considered using analogies drawn from an exploratory questionnaire survey. The objectives of a Customer Knowledge Management system could include increasing rental incomes and bringing new life back into shopping centers and towns
A Multi-factor Customer Classification Evaluation Model
Pervasive application of data mining technology is very important in analytical CRM software development when the distributed data warehouse is constructed. We propose a multi-factor customer classification evaluation model CLV/CL/CC which comprehensively considers customer lifetime value, customer loyalty and customer credit. It classifies clients with synthetic data mining algorithms. In this paper, we present an extended Bayes model which substitutes the primary attribute group with a new attribute group to improve the classification quality of naive Bayes
A Systematic Review of Consumer Behaviour Prediction Studies
Due to the importance of Customer behaviour prediction, it is
necessary to have a systematic review of previous studies on this subject. To
this effect, this paper therefore provides a systematic review of Customer
behaviours prediction studies with a focus on components of customer
relationship management, methods and datasets. In order to provide a
comprehensive literature review and a classification scheme for articles on this
subject 74 customer behaviour prediction papers in over 25 journals and
several conference proceedings were considered between the periods of 1999-
2014. Two hundred and thirty articles were identified and reviewed for their
direct relevance to predicting customer behaviour out of which 74 were
subsequently selected, reviewed and classified appropriately. The findings
show that the literature on predicting customer behaviour is ongoing and is of
most importance to organisation. It was observed that most studies investigated
customer retention prediction and organizational dataset were mostly used for
the prediction as compared to other form of dataset. Also, comparing the
statistical method to data mining in predicting customer behaviour, it was
discovered through this review that data mining is mostly used for prediction.
On the other hand, Artificial Neural Network is the most commonly used data
mining method for predicting customer behaviour. The review was able to
identify the limitations of the current research on the subject matter and
identify future research opportunities in customer behaviour prediction
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