scholarly journals Clustering and heterogeneous information fusion for social media theme discovery and associative mining

2014 ◽  
Author(s):  
Lei Meng
Keyword(s):  
Social Media ◽  
Information Fusion ◽  
Heterogeneous Information ◽  
Theme Discovery

scholarly journals A Survey of Data Mining Techniques on Information Networks

2018 ◽  
Vol 7 (2.6) ◽  
pp. 293
Author(s):  
Sadhana Kodali ◽  
Madhavi Dabbiru ◽  
B Thirumala Rao
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Networks ◽  
Information Network ◽  
Heterogeneous Information ◽  
Data Mining Techniques ◽  
Heterogeneous Information Networks ◽  
Web Objects ◽  
The Social ◽  
Social Media Network

An Information Network is the network formed by the interconnectivity of the objects formed due to the interaction between them. In our day-to-day life we can find these information networks like the social media network, the network formed by the interaction of web objects etc. This paper presents a survey of various Data Mining techniques that can be applicable to information networks. The Data Mining techniques of both homogeneous and heterogeneous information networks are discussed in detail and a comparative study on each problem category is showcased.

scholarly journals Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

2020 ◽  
Vol 11 ◽  
Author(s):  
Chunyu Wang ◽  
Jie Zhang ◽  
Xueping Wang ◽  
Ke Han ◽  
Maozu Guo
Keyword(s):  
Information Fusion ◽  
Gene Prediction ◽  
Prediction Algorithm ◽  
Heterogeneous Information ◽  
Pathogenic Gene

Web page and image semi-supervised classification with heterogeneous information fusion

2013 ◽  
Vol 39 (3) ◽  
pp. 289-306 ◽  
Author(s):  
Youtian Du ◽  
Chang Su ◽  
Zhongmin Cai ◽  
Xiaohong Guan
Keyword(s):  
Information Fusion ◽  
Supervised Classification ◽  
Web Page ◽  
Heterogeneous Information

scholarly journals Fake Research Detection using Weighting Algorithm in Netspam Framework

2019 ◽  
Vol 8 (2S11) ◽  
pp. 831-835
Keyword(s):  
Social Media ◽  
Service Providers ◽  
New Product ◽  
Business Strategies ◽  
Bayes Classifier ◽  
Search Query ◽  
Major Drawback ◽  
Heterogeneous Information ◽  
Point Of Interest ◽  
Related Feature

Now a day’s our life has become more dependent on social media. Social has opened many opportunity for business so, whenever customer wants to buy new product they will look for other people’s opinion. Social media has also have major drawback for business strategies which is spammers. Spammers create spam surveys about various products which mislead a consumer. This online opinion plays important role in business strategies, while positive opinion gives good publicity and market on the other side negative opinion gives bad publicity and market which affects the service providers. To avoid this spammers there have been many research but very have work on user and review related feature. In this investigation we propose a classification system using heterogeneous information network NetSpam framework. This system will classify spam and non-spam reviews using NetSpam algorithm and naïve bayes classifier for sentiment analysis which will provide positive and negative value of the product review. And furthermore if wants to search top product, user can use search query, in addition to that it will display recommendation product on the basis of user’s point of interest.

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