![Syllabus](../../_derived/syllabus.htm_cmp_deepblue000_vbtn.gif)
![Schedule](../../_derived/schedule.htm_cmp_deepblue000_vbtn.gif)
![Professor](../../_derived/professor.htm_cmp_deepblue000_vbtn.gif)
![Software](../../_derived/software.htm_cmp_deepblue000_vbtn.gif)
![Data](../../_derived/datasets.htm_cmp_deepblue000_vbtn.gif)
| |
Clustering
Clustering refers to classifying
items (cases or variables) into groups based on their similarities to others.
Topics
Readings
|
|
Handouts
Data
|
|
|
Bibliography
- Cowgill, G. L. 1968. Archaeological applications of factor, cluster, and
proximity analysis. American Antiquity 33:367-375.
- Abonyi, János& Feil, Balázs (2007). Cluster analysis for data mining and
system identification. Boston and Basel, Switzerland: Birkhäuser Basel.
- Aldenderfer, Mark S. and Roger K. Blashfield (1984). Cluster analysis.
Thousand Oaks, CA: Sage Publications, Quantitative Applications in the
Social Sciences Series No. 44.
- Anderberg, M. R. (1973). Cluster analysis for applications. New
York: Academic Press.
- Arabie, P.; Carroll, J. D.; & DeSarbo, W. S. (1987). Three-way
scaling and clustering. Beverly Hills, CA: Sage.
- Corter, James E. (1996). Tree models of similarity and association.
Thousand Oaks, CA: Sage Publications, Quantitative Applications in the
Social Sciences Series No. 112.
- Everitt B. S. (1980) Cluster Analysis,. London: Heinemann.
- Everitt, B. S., & Rabe-Hesketh, S. (1997). The analysis of proximity
data. London: Arnold.
- Everitt, Brian S., Sabine Landau, & Morven Leese (2001). Cluster
analysis, 4th Edition. London: Edward Arnold Publishers Ltd. Highly
recommended introductory text.
- Jain, A. K.& Dubey, R. C. (1988). Algorithms for clustering data.
Englewood Cliffs, NJ: Prentice Hall.
- Jajuga, Krzystof; Sokolowski; Andrzej; & Bock, Hans-Hermann (2002).
Classification, clustering and data analysis. Y: Springer.
- Kachigan, Sam K. (1982). Multivariate statistical analysis. NY:
Radius Press. Chapter 8 provides a very readable introduction to cluster
analysis.
- Kaufman, Leonard & Rousseeuw Peter J. (2005). Finding groups in data:
An introduction to cluster analysis NY: Wiley-Interscience.
- Melia, M. & Heckerman, D. (1998). An experimental comparison of
several clustering and initialization methods. Microsoft Research
Technical Report MSR-TR-98-06.
- Rapkin, B. D., & Luke, D. A. (1993). Cluster analysis in community
research: Epistemology and practice. American Journal of Community
Psychology 21, 247-277.
- Romesburg, Charles (2004). Cluster analysis for researchers.
Internet: lulu.com.
- Sarle, W.S & Kuo, An-Hsiang (1993). The MODECLUS procedure. SAS
Technical Report P-256, Cary, NC: SAS Institute Inc.
- Schneider, Andreas & Roberts, A. E. (2005). Classification and the
relations of meaning. Quality & Quantity 38(5): 547-557. Treats the
logic of K-means cluster analysis in the classification of affective
meanings:
- Sireci, S. G. & Geisinger , K. F. (1992). Analyzing test content using
cluster analysis and multidimensional scaling. Applied Psychological
Measurement 16(1), 17-31.
- Theodoridis, S. & Koutroumbas, K. (1999). Pattern recognition.
NY: Academic Press.
- Zhang, T.; Ramakrishnon, R.; & Livny, M. (1996). BIRCH: Method for very
large databases. Proceedings of the ACM. Management of Data. Pp.
103–114. Montreal, Canada.
|