LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Learning to construct knowledge bases from the World Wide Web
Artificial Intelligence - Special issue on Intelligent internet systems
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Discovering unexpected information from your competitors' web sites
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Framework for mining web content outliers
Proceedings of the 2004 ACM symposium on Applied computing
Mining web content outliers using structure oriented weighting techniques and N-grams
Proceedings of the 2005 ACM symposium on Applied computing
WCOND-Mine: Algorithm for Detecting Web Content Outliers from Web Documents
ISCC '05 Proceedings of the 10th IEEE Symposium on Computers and Communications
Personalized systems: models and methods from an IR and DB perspective
VLDB '05 Proceedings of the 31st international conference on Very large data bases
CWS: a comparative web search system
Proceedings of the 15th international conference on World Wide Web
An approach to extract special skills to improve the performance of resume selection
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
International Journal of Computational Science and Engineering
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In the E-commerce environment, a customer faces several difficulties for selecting the product. The technology like recommendation system is being developed to improve the performance of product selection. In this paper, we have investigated the problem of 'selecting a product from group of similar products' faced by the customer. For example, when a customer wants to buy a Sony camera through E-commerce Web site, the customer has to go through the information of several Sony camera models to select the appropriate one. In this paper, we have proposed an improved approach to help the customers to select the appropriate product. We have exploited the fact that every product possesses some specialness, which is exhibited through few special features. The proposed approach identifies the special features and organizes the features of the product in an effective manner. We have conducted the experiments on three real world data-sets related to Nokia-mobile phones, Sony-cameras and HP-laptops. The results indicate that the proposed approach has a potential to improve the performance of the product selection.