초록

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Abstract ▼ AI-Helper

We made an analysis on the pattern of on-line news which change as time flows. The record for the number of mentions in a series of time for short units of text that propagate and diffuse over the web from mainstream media to blogs is referred as “meme” data. The flow of changes in the number of quo...

We made an analysis on the pattern of on-line news which change as time flows. The record for the number of mentions in a series of time for short units of text that propagate and diffuse over the web from mainstream media to blogs is referred as “meme” data. The flow of changes in the number of quotes of these meme data is clustered using EM, K-means, X-means algorithm, and their patterns are compared. We also propose a prediction system for classifying the pattern of an independent meme with a set of training data using Naive Bayes, SMO, Random Forest. The training data set consists of the meme data where only the data prior to the peak are used, and the class label from the clustering. We validated the prediction system with Precision, Recall, F_measure, Accuracy using LOOCV. The accuracy rate is 95.3% when SMO classification algorithm is executed with the training data from K-means clustering algorithm. Our system will successfully predict the pattern of the independent meme data posterior to the peak.

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