Generic Feature Extraction for Classification using Fuzzy C - Means Clustering

Knowledge discovery and data mining (KDD) process includes preprocessing, transformation, data mining and knowledge extraction. The two important tasks of data mining are clustering and classification. In this paper, we propose a generic feature extraction for classification using fuzzy C-means (FCM) clustering. The raw data is preprocessed, normalized and then data points are clustered using the fuzzy C-means technique. Feature vectors for all the classes are generated by extracting the most relevant features from the corresponding clusters and used for further classification. Artificial neural network and support vector machines are used to perform the classification task. Experiments are conducted on four datasets and the accuracy obtained by performing specific feature extraction for a particular data set is compared with the generic feature extraction scheme. The algorithm performs relatively well with respect to classification results when compared with the specific feature extraction technique