Unsupervised learning aided by hierarchical analysis in knowledge exploration

Yihao Zhang*, Mehmet A. Orgun, Weiqiang Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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Abstract

Unsupervised learning plays an important role in knowledge exploration and discovery. Two basic examples of unsupervised learning are clustering and dimensionality reduction. In this paper, we introduce an improved model for clustering based on a hierarchical analysis method. In our model, there are three main steps. In the first step, we use a structural clustering model to find qualitative patterns from a given dataset. Then, the second step applies a quantitative-based clustering algorithm to find quantitative patterns from the dataset. The third and the last step generates hybrid patterns by combining the patterns obtained from the first two steps based on a certain criterion so that deeply hidden relationships can be extracted from the dataset. In this paper, we also discuss the results of our experiments with the proposed model and algorithms on longitudinal medical records.

Original languageEnglish
Title of host publicationProceedings - ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications
EditorsYuehui Chen, Aijith Abraham
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages661-665
Number of pages5
Volume1
ISBN (Print)0769525288, 9780769525280
DOIs
Publication statusPublished - 2006
EventISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications - Jinan, China
Duration: 16 Oct 200618 Oct 2006

Other

OtherISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications
CountryChina
CityJinan
Period16/10/0618/10/06

Bibliographical note

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