AN OPTIMAL FORECASTING OF LATENT SIMILARITY BASED RELATIONSHIP ON USER ATTRIBUTES USING LINK PREDICTION

P.Sathish Kumar; T. Suvathi

doi:10.29121/ijetmr.v4.i11.2017.122

Authors

P.Sathish Kumar Associate Professor, K.S.Rangasamy College of Technology, India
T.Suvathi PG Student, K.S.Rangasamy College of Technology, India

DOI:

https://doi.org/10.29121/ijetmr.v4.i11.2017.122

Keywords:

Communal, FGM Algorithm, Decision Tree

Abstract

Communal is one of the common words. A billion of peoples share or have certain attitudes and interests in common. By sharing and receiving the information such as text, image, audio, video etc., this kind of information, it improves the global knowledge, easily distinct the good and bad things. Now days, Social media is a good platform to share the content collectively with collaboration. Digital technologies are spread all over the global rapidly. It is an efficient way to improve the knowledge via Communal. People do not show the attention to join the community because of addiction, hacks the personal data and get misused. So that people have lack of awareness to join and use the communities. To overcome the above reasons and also all the peoples have to access and gain the information without any dilemmas. The proposed system provides the platform to link the peoples via Communal much more and gather the information all over the world with secure authentication. Anywhere in the world, every person can share and learn their thoughts with everyone. It consist of two phase to implement the proposed system. The first phase is to identify the neighbourhood and link the data. Here use Interest based FGM algorithm to predict the neighbour and link within the environment. So that each person will know all the information. Second phase, decision process to detect the person who are all link with particular communities across globally with the help of decision tree. People from anywhere to access all the data with anyone. It is easy way to equip people in all kind of innovative ideas as soon as possible.

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