Unsupervised data mining: introduction
Coomans, D., Smyth, C., Lee, I., Hancock, T., and Yang, J. (2009) Unsupervised data mining: introduction. In: Brown, Steven D., Tauler, Roma, and Walczak, Beata, (eds.) Comprehensive Chemometrics: chemical and biochemical data analysis. Elsevier, Oxford, UK, pp. 559-576.
![[img]](https://researchonline.jcu.edu.au/style/images/fileicons/application_pdf.png) |
PDF (Published Version)
Restricted to Repository staff only |
Abstract
This chapter focuses on cluster analysis in the context of unsupervised data mining. Various facets of cluster analysis, including proximities, are discussed in detail. Techniques of determining the natural number of clusters are described. Finally, techniques of assessing cluster accuracy and reproducibility are detailed. Techniques mentioned in this chapter are expanded upon in the following chapters.
| Item ID: | 8968 |
|---|---|
| Item Type: | Book Chapter (Reference) |
| ISBN: | 978-0-444-52701-1 |
| Keywords: | clustering; unsupervised learning; data mining; proximities |
| Related URLs: | |
| Date Deposited: | 20 Apr 2010 03:36 |
| FoR Codes: | 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100% |
| SEO Codes: | 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 100% |
| Downloads: |
Total: 5 |
| More Statistics |
Actions (Repository Staff Only)
 |
Item Control Page |