LGHALocal Government and Housing Act (UK)
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The main advantage of LGHA is to effectively increase the accuracy of unsupervised learning because the final result of knowledge exploration integrates the completed analysis from three levels.
In this section, Local-Global Hierarchical Analysis (LGHA) is utilized to assist unsupervised learning.
According to LGHA, we consider observed data as expressed in two patterns.
This section presents an experimental study on real-world datasets to test two analytical models, K-Means Clustering Analysis (KMeans) and Local-Global Hierarchical Analysis (LGHA).
Also, we sample patient's records and process them in LGHA. Table V (given on the previous page) shows some sample patient's records.
According to the theory of LGHA, we firstly investigate the qualitative pattern on state-space Si={Su, Ss, Sd} because only three states are designated for our analysis.
In this paper, we address two effective models, K-Means Clustering Analysis (K-Means) and Local-Global Hierarchical Analysis (LGHA) and combine them into a single framework to assist unsupervised learning.
We also build on a comprehensive structure and modeling procedure of LGHA (Lin and Orgun 2000, Lin and Orgun 2004).
We have examined the prospect of a combined system that can employ the model of K-Means and LGHA together.