Young-Jun Chung

A Proposal on the Error Bound of Collaborative Filtering Recommender System

We predict accuracy of users preferences by using memory-based collaborative filtering algorithm in recommender system, and then analyze the results through the EDA approach. The possibilities are presented that prediction accuracy can be evaluated before prediction process by analyzing the results. The classification functions using the generative probability of specific ratings are made, and users are classified by using the classification functions. The prediction accuracies of each classified group are analyzed through statistical tests. The method of setting the Error Bound of users who have high probabilities in low prediction accuracy will be presented.

An Energy Efficient Cluster Formation Algorithm for Wireless Sensor Networks

The efficient node energy utilization is one of important performance factors in wireless sensor networks because sensor nodes operate with limited battery power. To extend the lifetime of the wireless sensor networks, maintaining balanced power consumption between sensor nodes is more important than reducing each energy consumption of the sensor node in the network. In this paper, we proposed a cluster formation algorithm to extend the lifetime of the networks and to maintain a balanced energy consumption of nodes. To obtain it, we add a tiny slot in a round frame, which enables to exchange the residual energy messages between the base station (BS). cluster heads, and nodes. The performance of the proposed protocol has been examined and evaluated with the NS 2 simulator. As a result of simulation, we have confirmed that our proposed algorithm show the better performance in terms of lifetime than LEACH. Consequently, our proposed protocol can effectively extend the network lifetime without other critical overhead and performance degradation.