title: Clustering performance comparison usingK-means and expectation maximization algorithms.
abstract: Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are theK-means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and theK-means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.
authors.affiliation: Department of Medical IT Marketing, Eulji University , Sungnam , Korea.
authors.forename: Yong Gyu
authors.initials: YG
authors.lastname: Jung
authors.affiliation: Department of Medical IT Marketing, Eulji University , Sungnam , Korea.
authors.forename: Min Soo
authors.initials: MS
authors.lastname: Kang
authors.affiliation: Department of Information and Communication, Kyungmin University , Seoul , Korea.
authors.forename: Jun
authors.initials: J
authors.lastname: Heo
keywords: EM; K-means; logistic regression