K-means clustering based filter feature selection on high dimensional data - Dewi Pramudi Ismi; Shireen Panchoo; Murinto Murinto (Universitas Ahmad Dahlan, 2016)
With hundreds or thousands of features in high dimensional data, computational workload is challenging. In classification process, features which do not contribute significantly to prediction of classes, add to the computational workload. Therefore the aim of this paper is to use feature selection to decrease the computation load by reducing the size of high dimensional data. Selecting subsets of features which represent all features were used. Hence the process is two-fold; discarding irrelevant data and choosing one feature that representing a number of redundant features. There have been many studies regarding feature selection, for example backward feature selection and forward feature selection. In this study, a k-means clustering based feature selection is proposed. It is assumed that redundant features are located in the same cluster, whereas irrelevant features do not belong to any clusters. In this research, two different high dimensional datasets are used: 1) the Human Activity Recognition Using Smartphones (HAR) Dataset, containing 7352 data points each of 561 features and 2) the National Classification of Economic Activities Dataset, which contains 1080 data points each of 857 features. Both datasets provide class label information of each data point. Our experiment shows that k-means clustering based feature selection can be performed to produce subset of features. The latter returns more than 80% accuracy of classification result.