Data Classification Of Neuro Fuzzy Link Based Classification Algorithm
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To predict the links in the dataset we have used the Fuzzy Link Based Classification algorithm- a subpart of Neuro Fuzzy Link Based Classification algorithm, which is a combination on the Feedforward Neural Networks (FFNet) Backpropagation techniques and fuzzy logic. FFNet was inspired from the neural system the human body. In this chapter we will first explain about the system design involved in setting up the network followed by explaining the FFNet and Backpropagation algorithm, explain the reasons for using the algorithm and then discuss about how we worked on our dataset and the steps involved in obtaining the desired output.
5.1 System Design
From selecting the dataset to be worked on to data classification and link prediction, many steps are involved in like clustering data, classification of data, data extraction etc. These steps are performed in a proper order as mentioned in the below figure of System architecture.
Fig 5.1 System Architecture 
• Initially the dataset must be selected to retrieve the data so that classification can be performed.
• In user interface model data is retrieved from the dataset and represented in a readable format. On this data pattern recognition and analysis can be performed.
• In clustering and classification phase dissimilar data is differentiated from similar data. In our project this step can be omitted because the dataset is already in the form of a CSV file, divided according to the respective link types.