OWA-weighted based clustering method for classification problem

information classification is an important role in decision-making problems. As information technology advances, large amounts of information stored in database. Many tasks are worked out in high complexity and dimensionality in classification problem. Therefore, the paper applies ordered weighted averaging (OWA) operator to fusion multi-attribute data into the aggregated values of single attribute, and cluster the aggregated values for classification tasks. The proposed method consists of four steps: (1) use step-wise regression to select and order the important attribute, (2) utilize OWA operator to get aggregated values of single attribute from multi-attribute data, (3) cluster the aggregated values by K-means method, (4) predict the clusters of testing data. In verification and comparison, three datasets: (1) Iris, (2) Wisconsin-breast-cancer, and (3) Key Performance indicators datasets are conducted by the proposed method. The problems of high complexity and dimensionality are solved and the classification accuracy rate is higher than some existing methods. (C) 2008 Elsevier Ltd. All rights reserved.