A New Integrated Machine Learning Approach for Web Page Categorization

––Clustering is an unsupervised task whereas classification is supervised in nature. In the context of machine learning, classification of instances of a dataset is carried out by a classifier after the classifier is made to learn the model from a training dataset. The training data consists of instances which are labeled by a human expert. The labels are the classes into which the instances of the dataset are divided and are fixed by the human expert. The essence is that human intervention is required in the form of preparing the training data for the machine to carry out the task of classification. Clustering of large datasets is universally accepted as a difficult problem, since it tries to group instances together, without the helping hand of the human supervisor. Also, the time complexity of algorithms such as K-Medoids is unacceptably high, even for moderately large datasets. The work reported in this paper aims to integrate both clustering and classification and test approach in the domain of web page categorization. Rather than using training data created by a human expert for classification, clustering is used in preparing the training data for the classifier.