Clustering methods such as k-means and EM are the group of classification and pattern recognition, which are used in management science and literature search widely. In this paper, k-means and EM algorithm are compared the performance using by Weka. The winning Lottery numbers of 567 cases are experimented for our study and presentation. Processing speed of the k-means algorithm is superior to the EM algorithm, which is about 0.08 seconds faster than the other. As the result it is summerized that EM algorithm is better than K-means algorithm with comparison of accuracy, precision and recall. While K-means is known to be sensitive to the distribution of data, EM algorithm is probability sensitive for clustering .
목차
Abstract 1. Introduction 2. Related research 2.1 K-Means 2.2 EM 3. Experiment 4. Experimental Result 5. Conclusion References
키워드
Lotterygamblingk-meansEM AlgoritmWeka
저자
Yong Gyu Jung [ Dept. of Medical IT Marketing, Eulji University, Korea ]
Corresponding Author
Soo Ji Han [ Dept. of Medical IT Marketing, Eulji University, Korea ]
Jae Hee kim [ Chief executive officer, T2L Corp, Korea ]