Unsupervised learning aided by clustering and local-global hierarchical analysis in knowledge exploration

Unsupervised learning plays an important role in the Knowlede exploration discovery. The basic task of unsupervised learning is to find latent variablesor relationships in a given dataset wihout any assumed regularities or patterns. In this paper we apply two advanced models, clustering analysis and hierarchial analysis to accomplish unsupervised learning. K-Means clustering presents its strength in large scale clustering. The original data can be pre-processed and the potential variables are targeted. Correlations among these variables are explored in the subsequent sets by Local Global Hierarchial Analysis (LGHA) assisted by three main steps. In the first step, we use a structural approach to find qualititative patterns from the given variables. Then, the second step applies a quantitative based algorithm to find quantitative patterns from those variables. The and last step generated global hybrid patterns by combining the local patterns obtained from the first two steps based on a certain criterion. Both of the K-Means and Local Global Hierarchial Analysis (LGHA) models are applied in experiments with real world longitutional medical datasets.