Unsupervised Learning Aided by Clustering and Local-Global Hierarchical Analysis in Knowledge Exploration

1. Introduction From a traditional point of view, knowledge exploration can be categorized into supervised learning and unsupervised learning (Jordan and Jacobs 1994). In the last decade, there have been research activities on supervised learning approaches and techniques, whereby class information is available before any knowledge exploration takes place. The most utilized approach is to achieve a predetermined independent measurement in order to preferentially target classes. Then a classification algorithm is applied in the data pre-processing stage (Liu and Motoda 1998, Liu and Yu 2005). However, this approach is not robust to be effectively applied on features with irregular sizes or nonrecurring, high-dimensional variables. Unsupervised learning is a recent approach in knowledge exploration. It is widely used on/with unlabeled data, such as extracting relevance that exists in records. Unsupervised learning is an important supplementary method to category data since it could increase the precision of clustering results. Unlike supervised learning, unsupervised learning attempts