A Generalized K-Means Algorithm with Semi-Supervised Weight Coefficients
Fujiki Morii · 2006
A new classification algorithm corresponding to a generalization of the K-means algorithm is proposed, whose algorithm is named as a weighted K-means algorithm. Weight coefficients, which provide weighted distortions between data and cluster centers, are incorporated into the algorithm to realize reliable classification. A method determining the appropriate values of the weight coefficients from class labeled data is introduced. Under the situations where statistical distributions of data are changing gradually with time, the weighted K-means algorithm for semi-supervised data composed from initial labeled data and succeeding unlabeled data is investigated.