ANALYSIS AND PREDICTION OF TELEVISION SHOW POPULARITY RATING USING INCREMENTAL K-MEANS ALGORITHM

The Television Reality shows are increasing day-by-day in the present generations. There are many different ways to find the Television Rating Point (TRP). Firstly the raw data is taken based upon the People's Meter and the no of views will be counted from that. Then we need to divide the whole data set into clusters based on different channels. Here the data set consists of channels. Select the particular channel and take the view count. Depending upon the number of views, rate the channel or show accordingly, like if the view count is more than 10,000, then allot 10 rating to that particular show. If any new data is present then add it in the middle of the proces, then the whole process starts again. With the help of proposed algorithm we can update, add new entries in the middle of the process also. Based on the number of views we will rate that the particular Television shows accordingly with the highest Rating. The TRP can be compared among different shows and be viewed in bar graphs, pie charts, histograms. We have K-Means and Incremental K-Means algorithms to compare the TRP. The comparison between the two algorithms is very clear on histograms. It is the easiest way of predicting TV show analysis. If the data is inaccurate it may result to fault values.