%0 Journal Article %1 Nazema_2015 %A Nazema, Syeda %A Subhan, Syed. %A Deshmukh, S. N. %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K (k-Nearest-Neighbor) Analysis Domain Extraction Feature Incremental Recommendation System algorithm association clustering diffusive kNN mining rule %N 3 %P 1662--1668 %R 10.17762/ijritcc2321-8169.1503167 %T A Survey on Feature Recommendation Techniques %U http://dx.doi.org/10.17762/ijritcc2321-8169.1503167 %V 3 %X Recommendation systems are a very common now days and it is used in a variety of applications. A recommender system that is designed to reduce the human effort of performing domain analysis. Domain analysis is the task in which we can find the commonality and difference between the different software’s of same domain ‘feature recommendation is very useful now a days. This approach relies on data mining techniques to discover common features across products as well as the relationship among these common features. In this paper we used different techniques which are used for domain analysis and feature recommendation. This approach mines descriptions of product from publicly available online product descriptions, uses a text mining and a novel incremental diffusive clustering algorithm to discover features in specific domain , uses association rule mining to know latent relationships between the features within the products of same domain and uses KNN algorithm which generates a probabilistic feature model that represents commonalities, variant.

@article{Nazema_2015, abstract = {Recommendation systems are a very common now days and it is used in a variety of applications. A recommender system that is designed to reduce the human effort of performing domain analysis. Domain analysis is the task in which we can find the commonality and difference between the different software’s of same domain ‘feature recommendation is very useful now a days. This approach relies on data mining techniques to discover common features across products as well as the relationship among these common features. In this paper we used different techniques which are used for domain analysis and feature recommendation. This approach mines descriptions of product from publicly available online product descriptions, uses a text mining and a novel incremental diffusive clustering algorithm to discover features in specific domain , uses association rule mining to know latent relationships between the features within the products of same domain and uses KNN algorithm which generates a probabilistic feature model that represents commonalities, variant.}, added-at = {2015-08-13T08:31:44.000+0200}, author = {Nazema, Syeda and Subhan, Syed. and Deshmukh, S. N.}, biburl = {https://www.bibsonomy.org/bibtex/20ebc4fe27633b0e32507aab5d9a45c18/ijritcc}, doi = {10.17762/ijritcc2321-8169.1503167}, interhash = {0c21d93324fd7bba678140ee7902d3f6}, intrahash = {0ebc4fe27633b0e32507aab5d9a45c18}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {(k-Nearest-Neighbor) Analysis Domain Extraction Feature Incremental Recommendation System algorithm association clustering diffusive kNN mining rule}, month = {march}, number = 3, pages = {1662--1668}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-13T08:31:44.000+0200}, title = {A Survey on Feature Recommendation Techniques}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.1503167}, volume = 3, year = 2015 }